diff --git a/.github/workflows/cleanup.yaml b/.github/workflows/cleanup.yaml
index 1151f5f..5f49620 100644
--- a/.github/workflows/cleanup.yaml
+++ b/.github/workflows/cleanup.yaml
@@ -89,4 +89,9 @@ jobs:
         run: pipenv run sceptre --debug --var namespace=${{ github.event.ref }} delete develop/namespaced --yes
 
       - name: Remove artifacts
-        run: pipenv run python src/scripts/manage_artifacts/artifacts.py --remove --namespace ${{ github.event.ref }} --cfn_bucket ${{ vars.CFN_BUCKET }}
+        run: |
+          pipenv run python src/scripts/manage_artifacts/artifacts.py
+            --remove
+            --namespace ${{ github.event.ref }}
+            --cfn_bucket ${{ vars.CFN_BUCKET }}
+            --shareable-artifacts-bucket ${{ vars.SHAREABLE_ARTIFACTS_BUCKET }}
diff --git a/.github/workflows/upload-and-deploy-to-prod-main.yaml b/.github/workflows/upload-and-deploy-to-prod-main.yaml
index 929940b..e8cfdf3 100644
--- a/.github/workflows/upload-and-deploy-to-prod-main.yaml
+++ b/.github/workflows/upload-and-deploy-to-prod-main.yaml
@@ -41,7 +41,12 @@ jobs:
           python_version: ${{ env.PYTHON_VERSION }}
 
       - name: Copy files to templates bucket
-        run: python src/scripts/manage_artifacts/artifacts.py --upload --namespace $NAMESPACE --cfn_bucket ${{ vars.CFN_BUCKET }}
+        run: >
+          python src/scripts/manage_artifacts/artifacts.py
+          --upload
+          --namespace $NAMESPACE
+          --cfn_bucket ${{ vars.CFN_BUCKET }}
+          --shareable-artifacts-bucket ${{ vars.SHAREABLE_ARTIFACTS_BUCKET }}
 
 
   sceptre-deploy-main:
diff --git a/.github/workflows/upload-and-deploy.yaml b/.github/workflows/upload-and-deploy.yaml
index 5420909..ea8b877 100755
--- a/.github/workflows/upload-and-deploy.yaml
+++ b/.github/workflows/upload-and-deploy.yaml
@@ -115,6 +115,7 @@ jobs:
           --upload
           --namespace $NAMESPACE
           --cfn_bucket ${{ vars.CFN_BUCKET }}
+          --shareable-artifacts-bucket ${{ vars.SHAREABLE_ARTIFACTS_BUCKET }}
 
   nonglue-unit-tests:
     name: Runs unit tests that are not dependent on aws-glue package resources
@@ -437,6 +438,7 @@ jobs:
           --upload
           --namespace staging
           --cfn_bucket ${{ vars.CFN_BUCKET }}
+          --shareable-artifacts-bucket ${{ vars.SHAREABLE_ARTIFACTS_BUCKET }}
 
       - name: Create directory for remote sceptre templates
         run: mkdir -p templates/remote/
diff --git a/Pipfile b/Pipfile
index 1afc004..4a3913f 100644
--- a/Pipfile
+++ b/Pipfile
@@ -9,10 +9,11 @@ python_version = "3.9"
 [dev-packages]
 pytest = "*"
 pyarrow = "~=11.0"
-pre-commit = "*"
+pre-commit = "~=4.0"
 sceptre = ">=3.2.0"
 sceptre-sam-handler = "*"
 synapseclient = "~=2.7"
+numpy = "<2.0" # See issue "A module that was compiled using NumPy 1.x cannot be run..."
 pandas = "<1.5"
 moto = "~=4.1"
 datacompy = "~=0.8"
diff --git a/Pipfile.lock b/Pipfile.lock
index 9464b52..ed615af 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "f099bcf5b668dc8f3196d9738600c180c1aecc6f137ac9ea50c506e6fdd7b303"
+            "sha256": "76e9fb55a63bc554b8f9bbadd48253b27beac95c9180b5ad142c6f49bdda4ccc"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -19,11 +19,11 @@
     "develop": {
         "adagio": {
             "hashes": [
-                "sha256:c6c4d812f629fc3141284a0b3cfe483731b28da3a1b18f3d5498695ff87dcc12",
-                "sha256:e58abc4539184a65faf9956957d3787616bedeb1303ac5c9b1a201d8af6b87d7"
+                "sha256:0c32768f3aba0e05273b36f9420a482034f2510f059171040d7e98ba34128d7a",
+                "sha256:1bb8317d41bfff8b11373bc03c9859ff166c498214bb2b7ce1e21638c0babb2c"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==0.2.4"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.2.6"
         },
         "appdirs": {
             "hashes": [
@@ -34,11 +34,11 @@
         },
         "attrs": {
             "hashes": [
-                "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30",
-                "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"
+                "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346",
+                "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==23.2.0"
+            "version": "==24.2.0"
         },
         "blinker": {
             "hashes": [
@@ -50,86 +50,100 @@
         },
         "boto3": {
             "hashes": [
-                "sha256:81518aa95fad71279411fb5c94da4b4a554a5d53fc876faca62b7b5c8737f1cb",
-                "sha256:f79c15e33eb7706f197d98d828b193cf0891966682ad3ec5e900f6f9e7362e35"
+                "sha256:2d5e160b614db55fbee7981001c54476cb827c441cef65b2fcb2c52a62019909",
+                "sha256:7d9c359bbbc858a60b51c86328db813353c8bd1940212cdbd0a7da835291c2e1"
             ],
             "index": "pypi",
-            "markers": "python_version >= '3.8'",
-            "version": "==1.34.138"
+            "version": "==1.35.54"
         },
         "botocore": {
             "hashes": [
-                "sha256:84e96a954c39a6f09cae4ea95b2ae582b5ae01b5040c92507b60509c9be5377a",
-                "sha256:f558bbea96c4a4abbaeeedc477dabb00902311ba1ca6327974a6819b9f384920"
+                "sha256:131bb59ce59c8a939b31e8e647242d70cf11d32d4529fa4dca01feea1e891a76",
+                "sha256:9cca1811094b6cdc144c2c063a3ec2db6d7c88194b04d4277cd34fc8e3473aff"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==1.34.138"
+            "version": "==1.35.54"
         },
         "certifi": {
             "hashes": [
-                "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
-                "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"
+                "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8",
+                "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==2024.6.2"
+            "version": "==2024.8.30"
         },
         "cffi": {
             "hashes": [
-                "sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc",
-                "sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a",
-                "sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417",
-                "sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab",
-                "sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520",
-                "sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36",
-                "sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743",
-                "sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8",
-                "sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed",
-                "sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684",
-                "sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56",
-                "sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324",
-                "sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d",
-                "sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235",
-                "sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e",
-                "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088",
-                "sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000",
-                "sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7",
-                "sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e",
-                "sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673",
-                "sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c",
-                "sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe",
-                "sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2",
-                "sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098",
-                "sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8",
-                "sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a",
-                "sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0",
-                "sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b",
-                "sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896",
-                "sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e",
-                "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9",
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-                "sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f",
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-                "sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357"
+                "sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8",
+                "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2",
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+                "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824",
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+                "sha256:31000ec67d4221a71bd3f67df918b1f88f676f1c3b535a7eb473255fdc0b83fc",
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+                "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93",
+                "sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e",
+                "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4",
+                "sha256:c7eac2ef9b63c79431bc4b25f1cd649d7f061a28808cbc6c47b534bd789ef964",
+                "sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c",
+                "sha256:ca74b8dbe6e8e8263c0ffd60277de77dcee6c837a3d0881d8c1ead7268c9e576",
+                "sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0",
+                "sha256:cdf5ce3acdfd1661132f2a9c19cac174758dc2352bfe37d98aa7512c6b7178b3",
+                "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662",
+                "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3",
+                "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff",
+                "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5",
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+                "sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f",
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+                "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d",
+                "sha256:e221cf152cff04059d011ee126477f0d9588303eb57e88923578ace7baad17f9",
+                "sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7",
+                "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382",
+                "sha256:f1e22e8c4419538cb197e4dd60acc919d7696e5ef98ee4da4e01d3f8cfa4cc5a",
+                "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e",
+                "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a",
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             "markers": "platform_python_implementation != 'PyPy'",
-            "version": "==1.16.0"
+            "version": "==1.17.1"
         },
         "cfgv": {
             "hashes": [
@@ -148,99 +162,114 @@
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@@ -260,50 +289,44 @@
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-            "version": "==42.0.8"
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         "datacompy": {
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-                "sha256:b3eeb4ff15eec7fe645862f704256edaa301c3b872077ae92d2965064ed6af93"
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-            "markers": "python_full_version >= '3.9.0'",
-            "version": "==0.13.1"
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         "deepdiff": {
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@@ -330,10 +353,10 @@
         },
         "distlib": {
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-            "version": "==0.3.8"
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@@ -341,7 +364,6 @@
                 "sha256:aecd2277b8bf8e506e484f6ab7aec39abe0038e29fa4a6d3ba86c3fe01844ed9"
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@@ -350,24 +372,23 @@
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             "markers": "python_version < '3.11'",
-            "version": "==1.2.1"
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-                "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"
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             "markers": "python_version >= '3.8'",
-            "version": "==3.15.4"
+            "version": "==3.16.1"
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@@ -375,7 +396,6 @@
                 "sha256:f69fcd559dc907ed196ab9df0e48471709175e696d6e698dd4dbe940f96ce66b"
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-            "markers": "python_version >= '3.8'",
             "version": "==2.3.3"
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         "flask-cors": {
@@ -395,11 +415,11 @@
         },
         "fsspec": {
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             "markers": "python_version >= '3.8'",
-            "version": "==2024.6.1"
+            "version": "==2024.10.0"
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         "fugue": {
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@@ -411,26 +431,26 @@
         },
         "identify": {
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-            "version": "==2.5.36"
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-            "markers": "python_version >= '3.5'",
-            "version": "==3.7"
+            "markers": "python_version >= '3.6'",
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-            "markers": "python_version >= '3.7'",
+            "markers": "python_version < '3.10'",
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@@ -449,14 +469,6 @@
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@@ -488,79 +500,72 @@
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@@ -568,7 +573,6 @@
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@@ -626,7 +630,7 @@
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@@ -639,11 +643,11 @@
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@@ -670,16 +674,15 @@
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@@ -691,24 +694,23 @@
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@@ -739,7 +741,6 @@
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@@ -750,14 +751,6 @@
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@@ -798,84 +791,85 @@
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-            "markers": "python_version >= '3.6'",
-            "version": "==6.0.1"
+            "markers": "python_version >= '3.8'",
+            "version": "==6.0.2"
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         "requests": {
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@@ -895,20 +889,19 @@
         },
         "s3transfer": {
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-            "version": "==0.10.2"
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-                "sha256:5bb5683233346dc9bb7584d817851403f7b1c483d4d3ace805c76ebd09b9a49a",
-                "sha256:b5072ca390640f9966bd0277fa3ef26285f741557d8814ef51a81fa715925c6f"
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-            "markers": "python_version >= '3.8' and python_version < '4.0'",
-            "version": "==4.4.2"
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@@ -930,31 +923,22 @@
                 "sha256:f7ed9f80fe7ed4b6b12ba215152d98661e634429958a40419250e085ae51bcad"
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             "version": "==1.16.0"
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@@ -963,16 +947,15 @@
                 "sha256:dd8b1a1b4667d08311bb651469431f43fe2eeab83c0ef1fe5a03c2929aeb26cd"
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-            "version": "==2.0.1"
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@@ -984,19 +967,19 @@
         },
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-            "version": "==1.26.19"
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-            "version": "==20.26.3"
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@@ -1008,11 +991,11 @@
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-            "version": "==3.0.3"
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@@ -1092,19 +1075,19 @@
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             ],
-            "markers": "python_version >= '3.4'",
-            "version": "==0.13.0"
+            "markers": "python_version >= '3.6'",
+            "version": "==0.14.2"
         },
         "zipp": {
             "hashes": [
-                "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19",
-                "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"
+                "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350",
+                "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==3.19.2"
+            "version": "==3.20.2"
         }
     }
 }
diff --git a/config/develop/namespaced/glue-job-run-great-expectations-on-parquet.yaml b/config/develop/namespaced/glue-job-run-great-expectations-on-parquet.yaml
index 1e6d461..d37da00 100644
--- a/config/develop/namespaced/glue-job-run-great-expectations-on-parquet.yaml
+++ b/config/develop/namespaced/glue-job-run-great-expectations-on-parquet.yaml
@@ -7,12 +7,16 @@ parameters:
   Namespace: {{ stack_group_config.namespace }}
   JobDescription: Runs great expectations on a set of data
   JobRole: !stack_output_external glue-job-role::RoleArn
-  TempS3Bucket: {{ stack_group_config.processed_data_bucket_name }}
+  ParquetBucket: {{ stack_group_config.processed_data_bucket_name }}
+  ShareableArtifactsBucket: {{ stack_group_config.shareable_artifacts_vpn_bucket_name }}
   S3ScriptBucket: {{ stack_group_config.template_bucket_name }}
   S3ScriptKey: '{{ stack_group_config.namespace }}/src/glue/jobs/run_great_expectations_on_parquet.py'
+  ExpectationSuiteKey: "{{ stack_group_config.namespace }}/src/glue/resources/data_values_expectations.json"
+  GXConfigKey: "{{ stack_group_config.namespace }}/src/glue/resources/great_expectations.yml"
   GlueVersion: "{{ stack_group_config.great_expectations_job_glue_version }}"
   AdditionalPythonModules: "great_expectations~=0.18,urllib3<2"
 stack_tags:
   {{ stack_group_config.default_stack_tags }}
 sceptre_user_data:
   dataset_schemas: !file src/glue/resources/table_columns.yaml
+  data_values_expectations: !file src/glue/resources/data_values_expectations.json
diff --git a/config/develop/namespaced/glue-workflow.yaml b/config/develop/namespaced/glue-workflow.yaml
index 6861a72..1f51a5b 100644
--- a/config/develop/namespaced/glue-workflow.yaml
+++ b/config/develop/namespaced/glue-workflow.yaml
@@ -20,8 +20,6 @@ parameters:
   CompareParquetMainNamespace: "main"
   S3SourceBucketName: {{ stack_group_config.input_bucket_name }}
   CloudformationBucketName: {{ stack_group_config.template_bucket_name }}
-  ShareableArtifactsBucketName: {{ stack_group_config.shareable_artifacts_vpn_bucket_name }}
-  ExpectationSuiteKey: "{{ stack_group_config.namespace }}/src/glue/resources/data_values_expectations.json"
 stack_tags:
   {{ stack_group_config.default_stack_tags }}
 sceptre_user_data:
diff --git a/config/prod/namespaced/glue-job-run-great-expectations-on-parquet.yaml b/config/prod/namespaced/glue-job-run-great-expectations-on-parquet.yaml
index f0e8dd2..93ea716 100644
--- a/config/prod/namespaced/glue-job-run-great-expectations-on-parquet.yaml
+++ b/config/prod/namespaced/glue-job-run-great-expectations-on-parquet.yaml
@@ -7,12 +7,16 @@ parameters:
   Namespace: {{ stack_group_config.namespace }}
   JobDescription: Runs great expectations on a set of data
   JobRole: !stack_output_external glue-job-role::RoleArn
-  TempS3Bucket: {{ stack_group_config.processed_data_bucket_name }}
+  ParquetBucket: {{ stack_group_config.processed_data_bucket_name }}
+  ShareableArtifactsBucket: {{ stack_group_config.shareable_artifacts_vpn_bucket_name }}
   S3ScriptBucket: {{ stack_group_config.template_bucket_name }}
   S3ScriptKey: '{{ stack_group_config.namespace }}/src/glue/jobs/run_great_expectations_on_parquet.py'
+  ExpectationSuiteKey: "{{ stack_group_config.namespace }}/src/glue/resources/data_values_expectations.json"
+  GXConfigKey: "{{ stack_group_config.namespace }}/src/glue/resources/great_expectations.yml"
   GlueVersion: "{{ stack_group_config.great_expectations_job_glue_version }}"
   AdditionalPythonModules: "great_expectations~=0.18,urllib3<2"
 stack_tags:
   {{ stack_group_config.default_stack_tags }}
 sceptre_user_data:
   dataset_schemas: !file src/glue/resources/table_columns.yaml
+  data_values_expectations: !file src/glue/resources/data_values_expectations.json
diff --git a/config/prod/namespaced/glue-workflow.yaml b/config/prod/namespaced/glue-workflow.yaml
index 3223adb..ca20b3f 100644
--- a/config/prod/namespaced/glue-workflow.yaml
+++ b/config/prod/namespaced/glue-workflow.yaml
@@ -20,8 +20,6 @@ parameters:
   CompareParquetMainNamespace: "main"
   S3SourceBucketName: {{ stack_group_config.input_bucket_name }}
   CloudformationBucketName: {{ stack_group_config.template_bucket_name }}
-  ShareableArtifactsBucketName: {{ stack_group_config.shareable_artifacts_vpn_bucket_name }}
-  ExpectationSuiteKey: "{{ stack_group_config.namespace }}/src/glue/resources/data_values_expectations.json"
 stack_tags:
   {{ stack_group_config.default_stack_tags }}
 sceptre_user_data:
diff --git a/src/glue/jobs/run_great_expectations_on_parquet.py b/src/glue/jobs/run_great_expectations_on_parquet.py
index 68c0e99..c2799b4 100644
--- a/src/glue/jobs/run_great_expectations_on_parquet.py
+++ b/src/glue/jobs/run_great_expectations_on_parquet.py
@@ -1,23 +1,16 @@
 import json
 import logging
+import os
 import sys
 from datetime import datetime
 from typing import Dict
 
 import boto3
 import great_expectations as gx
+import pyspark
+import yaml
 from awsglue.context import GlueContext
 from awsglue.utils import getResolvedOptions
-from great_expectations.core.batch import RuntimeBatchRequest
-from great_expectations.core.expectation_configuration import ExpectationConfiguration
-from great_expectations.core.run_identifier import RunIdentifier
-from great_expectations.core.yaml_handler import YAMLHandler
-from great_expectations.data_context.types.base import DataContextConfig
-from great_expectations.data_context.types.resource_identifiers import (
-    ExpectationSuiteIdentifier,
-    ValidationResultIdentifier,
-)
-from pyspark.context import SparkContext
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -39,6 +32,7 @@ def read_args() -> dict:
             "namespace",
             "data-type",
             "expectation-suite-key",
+            "gx-config-key",
         ],
     )
     for arg in args:
@@ -57,149 +51,75 @@ def validate_args(value: str) -> None:
     """
     if value == "":
         raise ValueError("Argument value cannot be an empty string")
-    else:
-        return None
+    return None
 
 
-def create_context(
-    s3_bucket: str, namespace: str, key_prefix: str
-) -> "EphemeralDataContext":
-    """Creates the data context and adds stores,
-        datasource and data docs configurations
-
-    Args:
-        s3_bucket (str): name of s3 bucket to store to
-        namespace (str): namespace
-        key_prefix (str): s3 key prefix
-
-    Returns:
-        EphemeralDataContext: context object with all
-            configurations
-    """
-    context = gx.get_context()
-    add_datasource(context)
-    add_validation_stores(context, s3_bucket, namespace, key_prefix)
-    add_data_docs_sites(context, s3_bucket, namespace, key_prefix)
-    return context
-
+def configure_gx_config(
+    gx_config_bucket: str,
+    gx_config_key: str,
+    shareable_artifacts_bucket: str,
+    namespace: str,
+) -> dict:
+    """Download and configure a `great_expectations.yml` file locally
 
-def add_datasource(context: "EphemeralDataContext") -> "EphemeralDataContext":
-    """Adds the spark datasource
+    This function will download a `great_expectations.yml` file from S3 to a `gx` directory.
+    This file will be automatically be used to configue the GX data context when calling
+    `gx.get_context()`.
 
     Args:
-        context (EphemeralDataContext): data context to add to
-
-    Returns:
-        EphemeralDataContext: data context object with datasource configuration
-            added
+        gx_config_bucket (str): S3 bucket containing the `great_expectations.yml` file.
+        gx_config_key (str): S3 key where this file is located.
+        shareable_artifacts_bucket (str): S3 bucket where shareable artifacts are written.
+        namespace (str): The current namespace
     """
-    yaml = YAMLHandler()
-    context.add_datasource(
-        **yaml.load(
-            """
-        name: spark_datasource
-        class_name: Datasource
-        execution_engine:
-            class_name: SparkDFExecutionEngine
-            force_reuse_spark_context: true
-        data_connectors:
-            runtime_data_connector:
-                class_name: RuntimeDataConnector
-                batch_identifiers:
-                    - batch_identifier
-        """
+    gx_config_path = "gx/great_expectations.yml"
+    os.makedirs("gx", exist_ok=True)
+    s3_client = boto3.client("s3")
+    logger.info(
+        f"Downloading s3://{gx_config_bucket}/{gx_config_key} to {gx_config_path}"
+    )
+    s3_client.download_file(
+        Bucket=gx_config_bucket, Key=gx_config_key, Filename=gx_config_path
+    )
+    with open(gx_config_path, "rb") as gx_config_obj:
+        gx_config = yaml.safe_load(gx_config_obj)
+    # fmt: off
+    gx_config["stores"]["validations_store"]["store_backend"]["bucket"] = (
+        shareable_artifacts_bucket
+    )
+    gx_config["stores"]["validations_store"]["store_backend"]["prefix"] = (
+        gx_config["stores"]["validations_store"]["store_backend"]["prefix"].format(
+            namespace=namespace
         )
     )
-    return context
-
-
-def add_validation_stores(
-    context: "EphemeralDataContext",
-    s3_bucket: str,
-    namespace: str,
-    key_prefix: str,
-) -> "EphemeralDataContext":
-    """Adds the validation store configurations to the context object
-
-    Args:
-        context (EphemeralDataContext): data context to add to
-        s3_bucket (str): name of the s3 bucket to save validation results to
-        namespace (str): name of the namespace
-        key_prefix (str): s3 key prefix to save the
-            validation results to
-
-    Returns:
-        EphemeralDataContext: data context object with validation stores'
-            configuration added
-    """
-    # Programmatically configure the validation result store and
-    # DataDocs to use S3
-    context.add_store(
-        "validation_result_store",
-        {
-            "class_name": "ValidationsStore",
-            "store_backend": {
-                "class_name": "TupleS3StoreBackend",
-                "bucket": s3_bucket,
-                "prefix": f"{namespace}/{key_prefix}",
-            },
-        },
+    gx_config["data_docs_sites"]["s3_site"]["store_backend"]["bucket"] = (
+        shareable_artifacts_bucket
     )
-    return context
-
-
-def add_data_docs_sites(
-    context: "EphemeralDataContext",
-    s3_bucket: str,
-    namespace: str,
-    key_prefix: str,
-) -> "EphemeralDataContext":
-    """Adds the data docs sites configuration to the context object
-        so data docs can be saved to a s3 location. This is a special
-        workaround to add the data docs because we're using EphemeralDataContext
-        context objects and they don't store to memory.
-
-    Args:
-        context (EphemeralDataContext): data context to add to
-        s3_bucket (str): name of the s3 bucket to save gx docs to
-        namespace (str): name of the namespace
-        key_prefix (str): s3 key prefix to save the
-            gx docs to
-
-    Returns:
-        EphemeralDataContext: data context object with data docs sites'
-            configuration added
-    """
-    data_context_config = DataContextConfig()
-    data_context_config["data_docs_sites"] = {
-        "s3_site": {
-            "class_name": "SiteBuilder",
-            "store_backend": {
-                "class_name": "TupleS3StoreBackend",
-                "bucket": s3_bucket,
-                "prefix": f"{namespace}/{key_prefix}",
-            },
-            "site_index_builder": {"class_name": "DefaultSiteIndexBuilder"},
-        }
-    }
-    context._project_config["data_docs_sites"] = data_context_config["data_docs_sites"]
-    return context
+    gx_config["data_docs_sites"]["s3_site"]["store_backend"]["prefix"] = (
+        gx_config["data_docs_sites"]["s3_site"]["store_backend"]["prefix"].format(
+            namespace=namespace
+        )
+    )
+    # fmt: on
+    with open(gx_config_path, "w", encoding="utf-8") as gx_config_obj:
+        yaml.dump(gx_config, gx_config_obj)
+    return gx_config
 
 
 def get_spark_df(
     glue_context: GlueContext, parquet_bucket: str, namespace: str, data_type: str
 ) -> "pyspark.sql.dataframe.DataFrame":
-    """Reads in the parquet dataset as a Dynamic Frame and converts it
-        to a spark dataframe
+    """
+    Read a data-type-specific Parquet dataset
 
     Args:
-        glue_context (GlueContext): the aws glue context object
-        parquet_bucket (str): the name of the bucket holding parquet files
-        namespace (str): the namespace
-        data_type (str): the data type name
+        glue_context (GlueContext): The AWS Glue context object
+        parquet_bucket (str): The S3 bucket containing the data-type-specific Parquet dataset
+        namespace (str): The associated namespace
+        data_type (str): The associated data type
 
     Returns:
-        pyspark.sql.dataframe.DataFrame: spark dataframe of the read in parquet dataset
+        pyspark.sql.dataframe.DataFrame: A Spark dataframe over our data-type-specific Parquet dataset
     """
     s3_parquet_path = f"s3://{parquet_bucket}/{namespace}/parquet/dataset_{data_type}/"
     dynamic_frame = glue_context.create_dynamic_frame_from_options(
@@ -212,29 +132,24 @@ def get_spark_df(
 
 
 def get_batch_request(
+    gx_context: gx.data_context.AbstractDataContext,
     spark_dataset: "pyspark.sql.dataframe.DataFrame",
     data_type: str,
-    run_id: RunIdentifier,
-) -> RuntimeBatchRequest:
-    """Retrieves the unique metadata for this batch request
+) -> gx.datasource.fluent.batch_request.BatchRequest:
+    """
+    Get a GX batch request over a Spark dataframe
 
     Args:
-        spark_dataset (pyspark.sql.dataframe.DataFrame): parquet dataset as spark df
-        data_type (str): data type name
-        run_id (RunIdentifier): contains the run name and
-            run time metadata of this batch run
+        spark_dataset (pyspark.sql.dataframe.DataFrame): A Spark dataframe
+        data_type (str): The data type
 
     Returns:
-        RuntimeBatchRequest: contains metadata for the batch run request
-            to identify this great expectations run
+        BatchRequest: A batch request which can be used in conjunction
+            with an expectation suite to validate our data.
     """
-    batch_request = RuntimeBatchRequest(
-        datasource_name="spark_datasource",
-        data_connector_name="runtime_data_connector",
-        data_asset_name=f"{data_type}-parquet-data-asset",
-        runtime_parameters={"batch_data": spark_dataset},
-        batch_identifiers={"batch_identifier": f"{data_type}_{run_id.run_name}_batch"},
-    )
+    data_source = gx_context.sources.add_or_update_spark(name="parquet")
+    data_asset = data_source.add_dataframe_asset(name=f"{data_type}_spark_dataframe")
+    batch_request = data_asset.build_batch_request(dataframe=spark_dataset)
     return batch_request
 
 
@@ -243,13 +158,13 @@ def read_json(
     s3_bucket: str,
     key: str,
 ) -> Dict[str, str]:
-    """Reads in a json object
+    """
+    Read a JSON file from an S3 bucket
 
     Args:
-        s3 (boto3.client): s3 client connection
-        s3_bucket (str): name of the s3 bucket to read from
-        key (str): s3 key prefix of the
-            location of the json to read from
+        s3 (boto3.client): An S3 client
+        s3_bucket (str): The S3 bucket containing the JSON file
+        key (str): The S3 key of the JSON file
 
     Returns:
         Dict[str, str]: the data read in from json
@@ -263,112 +178,65 @@ def read_json(
 
 def add_expectations_from_json(
     expectations_data: Dict[str, str],
-    context: "EphemeralDataContext",
-    data_type: str,
-) -> "EphemeralDataContext":
-    """Adds in the read in expectations to the context object
-
-    Args:
-        expectations_data (Dict[str, str]): expectations
-        context (EphemeralDataContext): context object
-        data_type (str): name of the data type
-
-    Raises:
-        ValueError: thrown when no expectations exist for this data type
-
-    Returns:
-        EphemeralDataContext: context object with expectations added
+    context: gx.data_context.AbstractDataContext,
+) -> gx.data_context.AbstractDataContext:
     """
-    # Ensure the data type exists in the JSON file
-    if data_type not in expectations_data:
-        raise ValueError(f"No expectations found for data type '{data_type}'")
-
-    # Extract the expectation suite and expectations for the dataset
-    suite_data = expectations_data[data_type]
-    expectation_suite_name = suite_data["expectation_suite_name"]
-    new_expectations = suite_data["expectations"]
-
-    # Convert new expectations from JSON format to ExpectationConfiguration objects
-    new_expectations_configs = [
-        ExpectationConfiguration(
-            expectation_type=exp["expectation_type"], kwargs=exp["kwargs"]
-        )
-        for exp in new_expectations
-    ]
-
-    # Update the expectation suite in the data context
-    context.add_or_update_expectation_suite(
-        expectation_suite_name=expectation_suite_name,
-        expectations=new_expectations_configs,
-    )
-    return context
-
-
-def add_validation_results_to_store(
-    context: "EphemeralDataContext",
-    expectation_suite_name: str,
-    validation_result: Dict[str, str],
-    batch_identifier: RuntimeBatchRequest,
-    run_identifier: RunIdentifier,
-) -> "EphemeralDataContext":
-    """Adds the validation results manually to the validation store.
-        This is a workaround for a EphemeralDataContext context object,
-        and for us to avoid complicating our folder structure to include
-        checkpoints/other more persistent data context object types
-        until we need that feature
+    Add an expectation suite with expectations to our GX data context for each data type.
 
     Args:
-        context (EphemeralDataContext): context object to add results to
-        expectation_suite_name (str): name of expectation suite
-        validation_result (Dict[str, str]): results outputted by gx
-            validator to be stored
-        batch_identifier (RuntimeBatchRequest): metadata containing details of
-            the batch request
-        run_identifier (RunIdentifier): metadata containing details of the gx run
+        expectations_data (Dict[str, str]): A mapping of data types to their expectations.
+            The expectations should be formatted like so:
+
+                {
+                    "expectation_suite_name": "string",
+                    "expectations": {
+                        "expectation_type": "str",
+                        "kwargs": "readable by `ExpectationConfiguration`"
+                    }
+                }
+        context (gx.data_context.AbstractDataContext): context object
 
     Returns:
-        EphemeralDataContext: context object with validation results added to
+        gx.data_context.AbstractDataContext: A GX data context object with expectation suites added
     """
-    expectation_suite = context.get_expectation_suite(expectation_suite_name)
-    # Create an ExpectationSuiteIdentifier
-    expectation_suite_identifier = ExpectationSuiteIdentifier(
-        expectation_suite_name=expectation_suite.expectation_suite_name
-    )
-
-    # Create a ValidationResultIdentifier using the run_id, expectation suite, and batch identifier
-    validation_result_identifier = ValidationResultIdentifier(
-        expectation_suite_identifier=expectation_suite_identifier,
-        batch_identifier=batch_identifier,
-        run_id=run_identifier,
-    )
-
-    context.validations_store.set(validation_result_identifier, validation_result)
+    for data_type in expectations_data:
+        suite_data = expectations_data[data_type]
+        expectation_suite_name = suite_data["expectation_suite_name"]
+        new_expectations = suite_data["expectations"]
+
+        # Convert new expectations from dict to ExpectationConfiguration objects
+        new_expectations_configs = [
+            gx.core.expectation_configuration.ExpectationConfiguration(
+                expectation_type=exp["expectation_type"], kwargs=exp["kwargs"]
+            )
+            for exp in new_expectations
+        ]
+
+        # Update the expectation suite in the data context
+        context.add_or_update_expectation_suite(
+            expectation_suite_name=expectation_suite_name,
+            expectations=new_expectations_configs,
+        )
     return context
 
 
 def main():
     args = read_args()
-    run_id = RunIdentifier(run_name=f"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
-    expectation_suite_name = f"{args['data_type']}_expectations"
     s3 = boto3.client("s3")
-    context = create_context(
-        s3_bucket=args["shareable_artifacts_bucket"],
-        namespace=args["namespace"],
-        key_prefix=f"great_expectation_reports/{args['data_type']}/parquet/",
+    run_id = gx.core.run_identifier.RunIdentifier(
+        run_name=f"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
     )
-    glue_context = GlueContext(SparkContext.getOrCreate())
-    logger.info("get_spark_df")
-    spark_df = get_spark_df(
-        glue_context=glue_context,
-        parquet_bucket=args["parquet_bucket"],
+    expectation_suite_name = f"{args['data_type']}_expectations"
+
+    # Set up Great Expectations
+    logger.info("configure_gx_config")
+    configure_gx_config(
+        gx_config_bucket=args["cfn_bucket"],
+        gx_config_key=args["gx_config_key"],
+        shareable_artifacts_bucket=args["shareable_artifacts_bucket"],
         namespace=args["namespace"],
-        data_type=args["data_type"],
     )
-    logger.info("get_batch_request")
-    batch_request = get_batch_request(spark_df, args["data_type"], run_id)
-    logger.info("add_expectations")
-
-    # Load the JSON file with the expectations
+    gx_context = gx.get_context()
     logger.info("reads_expectations_from_json")
     expectations_data = read_json(
         s3=s3,
@@ -376,32 +244,37 @@ def main():
         key=args["expectation_suite_key"],
     )
     logger.info("adds_expectations_from_json")
-    add_expectations_from_json(
+    gx_context = add_expectations_from_json(
         expectations_data=expectations_data,
-        context=context,
-        data_type=args["data_type"],
-    )
-    logger.info("get_validator")
-    validator = context.get_validator(
-        batch_request=batch_request,
-        expectation_suite_name=expectation_suite_name,
+        context=gx_context,
     )
-    logger.info("validator.validate")
-    validation_result = validator.validate()
 
-    logger.info("validation_result: %s", validation_result)
+    # Set up Spark
+    glue_context = GlueContext(pyspark.context.SparkContext.getOrCreate())
+    logger.info("get_spark_df")
+    spark_df = get_spark_df(
+        glue_context=glue_context,
+        parquet_bucket=args["parquet_bucket"],
+        namespace=args["namespace"],
+        data_type=args["data_type"],
+    )
 
-    add_validation_results_to_store(
-        context,
-        expectation_suite_name,
-        validation_result,
-        batch_identifier=batch_request["batch_identifiers"]["batch_identifier"],
-        run_identifier=run_id,
+    # Put the two together and validate the GX expectations
+    logger.info("get_batch_request")
+    batch_request = get_batch_request(
+        gx_context=gx_context, spark_dataset=spark_df, data_type=args["data_type"]
     )
-    context.build_data_docs(
-        site_names=["s3_site"],
+    logger.info("add_or_update_checkpoint")
+    # The default checkpoint action list is:
+    # StoreValidationResultAction, StoreEvaluationParametersAction, UpdateDataDocsAction
+    checkpoint = gx_context.add_or_update_checkpoint(
+        name=f"{args['data_type']}-checkpoint",
+        expectation_suite_name=expectation_suite_name,
+        batch_request=batch_request,
     )
-    logger.info("data docs saved!")
+    logger.info("run checkpoint")
+    checkpoint_result = checkpoint.run(run_id=run_id)
+    logger.info("data docs updated!")
 
 
 if __name__ == "__main__":
diff --git a/src/glue/resources/great_expectations.yml b/src/glue/resources/great_expectations.yml
new file mode 100644
index 0000000..d210b4c
--- /dev/null
+++ b/src/glue/resources/great_expectations.yml
@@ -0,0 +1,50 @@
+config_version: 3.0
+stores:
+  expectations_store:
+    class_name: ExpectationsStore
+    store_backend:
+      class_name: TupleFilesystemStoreBackend
+      base_directory: expectations/
+  validations_store:
+    class_name: ValidationsStore
+    store_backend:
+      class_name: TupleS3StoreBackend
+      suppress_store_backend_id: true
+      bucket: "{shareable_artifacts_bucket}"
+      prefix: "{namespace}/great_expectation_reports/parquet/validations/"
+  evaluation_parameter_store:
+    class_name: EvaluationParameterStore
+  checkpoint_store:
+    class_name: CheckpointStore
+    store_backend:
+      class_name: TupleFilesystemStoreBackend
+      base_directory: checkpoints/
+  profiler_store:
+    class_name: ProfilerStore
+    store_backend:
+      class_name: TupleFilesystemStoreBackend
+      base_directory: profilers/
+expectations_store_name: expectations_store
+validations_store_name: validations_store
+evaluation_parameter_store_name: evaluation_parameter_store
+checkpoint_store_name: checkpoint_store
+data_docs_sites:
+  s3_site:
+    class_name: SiteBuilder
+    store_backend:
+      class_name: TupleS3StoreBackend
+      bucket: "{shareable_artifacts_bucket}"
+      prefix: "{namespace}/great_expectation_reports/parquet/"
+    site_index_builder:
+      class_name: DefaultSiteIndexBuilder
+include_rendered_content:
+  globally: false
+  expectation_suite: false
+  expectation_validation_result: false
+fluent_datasources:
+  my_parquet_datasource:
+    type: spark
+    assets:
+      my_dataframe_asset:
+        type: dataframe
+        batch_metadata: {}
diff --git a/src/scripts/manage_artifacts/artifacts.py b/src/scripts/manage_artifacts/artifacts.py
index cef9399..0e11602 100755
--- a/src/scripts/manage_artifacts/artifacts.py
+++ b/src/scripts/manage_artifacts/artifacts.py
@@ -10,6 +10,7 @@ def read_args():
     parser = argparse.ArgumentParser(description="")
     parser.add_argument("--namespace")
     parser.add_argument("--cfn_bucket", required=True)
+    parser.add_argument("--shareable-artifacts-bucket", required=True)
     group = parser.add_mutually_exclusive_group(required=True)
     group.add_argument("--upload", action="store_true")
     group.add_argument("--remove", action="store_true")
@@ -18,9 +19,9 @@ def read_args():
     return args
 
 
-def execute_command(cmd: str):
+def execute_command(cmd: list[str]):
     print(f'Invoking command: {" ".join(cmd)}')
-    subprocess.run(cmd)
+    subprocess.run(cmd, check=True)
 
 
 def upload(namespace: str, cfn_bucket: str):
@@ -65,6 +66,7 @@ def main(args):
         upload(args.namespace, args.cfn_bucket)
     elif args.remove:
         delete(args.namespace, args.cfn_bucket)
+        delete(args.namespace, args.shareable_artifacts_bucket)
     else:
         list_namespaces(args.cfn_bucket)
 
diff --git a/templates/glue-job-run-great-expectations-on-parquet.j2 b/templates/glue-job-run-great-expectations-on-parquet.j2
index 455b5e8..a125857 100644
--- a/templates/glue-job-run-great-expectations-on-parquet.j2
+++ b/templates/glue-job-run-great-expectations-on-parquet.j2
@@ -19,9 +19,14 @@ Parameters:
     Type: String
     Description: The name or ARN of the IAM role that will run this job.
 
-  TempS3Bucket:
+  ParquetBucket:
     Type: String
-    Description: The name of the S3 bucket where temporary files and logs are written.
+    Description: The name of the S3 bucket where Parquet data is written. This is
+        also where we will write temporary files and logs.
+
+  ShareableArtifactsBucket:
+    Type: String
+    Description: The name of the bucket where shareable artifacts are stored.
 
   S3ScriptBucket:
     Type: String
@@ -48,6 +53,14 @@ Parameters:
         FitbitIntradayCombined and HealthKitV2Samples.
     Default: 1
 
+  ExpectationSuiteKey:
+    Type: String
+    Description: The S3 key of the GX expectation file.
+
+  GXConfigKey:
+    Type: String
+    Description: The S3 key of the GX configuration file/template.
+
   MaxRetries:
     Type: Number
     Description: How many times to retry the job if it fails (integer).
@@ -71,12 +84,11 @@ Resources:
   {% for v in sceptre_user_data.dataset_schemas.tables.keys() if not "Deleted" in v %}
     {% set dataset = {} %}
     {% do dataset.update({"type": v}) %}
-    {% do dataset.update({"table_name": "dataset_" + v.lower()})%}
     {% do dataset.update({"stackname_prefix": "{}".format(v.replace("_",""))}) %}
     {% do datasets.append(dataset) %}
   {% endfor %}
 
-  {% for dataset in datasets %}
+  {% for dataset in datasets if dataset["type"].lower() in sceptre_user_data.data_values_expectations %}
   {{ dataset["stackname_prefix"] }}GreatExpectationsParquetJob:
     Type: AWS::Glue::Job
     Properties:
@@ -84,15 +96,21 @@ Resources:
         Name: glueetl
         ScriptLocation: !Sub s3://${S3ScriptBucket}/${S3ScriptKey}
       DefaultArguments:
-        --TempDir: !Sub s3://${TempS3Bucket}/tmp
+        --TempDir: !Sub s3://${ParquetBucket}/tmp
         --enable-continuous-cloudwatch-log: true
         --enable-metrics: true
         --enable-spark-ui: true
-        --spark-event-logs-path: !Sub s3://${TempS3Bucket}/spark-logs/${AWS::StackName}/
+        --spark-event-logs-path: !Sub s3://${ParquetBucket}/spark-logs/${AWS::StackName}/
         --job-bookmark-option: job-bookmark-disable
         --job-language: python
         --additional-python-modules: !Ref AdditionalPythonModules
-        # --conf spark.sql.adaptive.enabled
+        --data-type: {{ dataset["type"].lower() }}
+        --namespace: !Ref Namespace
+        --cfn-bucket: !Ref S3ScriptBucket
+        --parquet-bucket: !Ref ParquetBucket
+        --shareable-artifacts-bucket: !Ref ShareableArtifactsBucket
+        --expectation-suite-key: !Ref ExpectationSuiteKey
+        --gx-config-key: !Ref GXConfigKey
       Description: !Sub "${JobDescription} for data type {{ dataset['type'] }}"
       GlueVersion: !Ref GlueVersion
       MaxRetries: !Ref MaxRetries
diff --git a/templates/glue-workflow.j2 b/templates/glue-workflow.j2
index 16d8950..5483368 100644
--- a/templates/glue-workflow.j2
+++ b/templates/glue-workflow.j2
@@ -40,7 +40,7 @@ Parameters:
 
   ParquetKeyPrefix:
     Type: String
-    Description: S3 key prefix where JSON datasets are stored.
+    Description: S3 key prefix where Parquet datasets are stored.
     Default: parquet
 
   GlueDatabase:
@@ -82,13 +82,6 @@ Parameters:
     Description: >-
         The name of the bucket where the cloudformation and artifacts are stored.
 
-  ShareableArtifactsBucketName:
-    Type: String
-    Description: The name of the bucket where shareable artifacts are stored.
-
-  ExpectationSuiteKey:
-    Type: String
-    Description: The s3 key prefix of the expectation suite.
 
 Conditions:
   IsMainNamespace: !Equals [!Ref Namespace, "main"]
@@ -316,14 +309,6 @@ Resources:
       Name: !Sub "${Namespace}-{{ dataset['stackname_prefix'] }}GreatExpectationsParquetTrigger"
       Actions:
         - JobName: !Sub ${Namespace}-{{ dataset["stackname_prefix"] }}-GreatExpectationsParquetJob
-          Arguments:
-            "--data-type": {{ dataset["data_type"].lower() }}
-            "--namespace": !Ref Namespace
-            "--cfn-bucket": !Ref CloudformationBucketName
-            "--parquet-bucket": !Ref ParquetBucketName
-            "--shareable-artifacts-bucket": !Ref ShareableArtifactsBucketName
-            "--expectation-suite-key": !Ref ExpectationSuiteKey
-            "--additional-python-modules": "great_expectations~=0.18,urllib3<2"
       Description:  This trigger runs the great expectation parquet job for this data type after completion of the JSON to Parquet job for this data type
       Type: CONDITIONAL
       Predicate:
diff --git a/tests/test_run_great_expectations_on_parquet.py b/tests/test_run_great_expectations_on_parquet.py
index 074e140..2168725 100644
--- a/tests/test_run_great_expectations_on_parquet.py
+++ b/tests/test_run_great_expectations_on_parquet.py
@@ -1,152 +1,169 @@
-from unittest.mock import MagicMock, patch
+import os
+import shutil
+import unittest
 
+import boto3
 import great_expectations as gx
+import pyspark
 import pytest
-from great_expectations.core.batch import RuntimeBatchRequest
-from great_expectations.core.run_identifier import RunIdentifier
-from great_expectations.core.yaml_handler import YAMLHandler
-from great_expectations.data_context.types.resource_identifiers import (
-    ExpectationSuiteIdentifier,
-    ValidationResultIdentifier,
-)
-from pyspark.sql import SparkSession
+import yaml
+from moto import mock_s3
 
 from src.glue.jobs import run_great_expectations_on_parquet as run_gx_on_pq
 
 
 @pytest.fixture
-def test_context(scope="function"):
+def gx_context(scope="function"):
     context = gx.get_context()
     yield context
 
 
 @pytest.fixture(scope="function")
-def test_spark():
-    yield SparkSession.builder.appName("BatchRequestTest").getOrCreate()
-
-
-def test_create_context():
-    with (
-        patch.object(gx, "get_context") as mock_get_context,
-        patch.object(run_gx_on_pq, "add_datasource") as mock_add_datasource,
-        patch.object(
-            run_gx_on_pq, "add_validation_stores"
-        ) as mock_add_validation_stores,
-        patch.object(run_gx_on_pq, "add_data_docs_sites") as mock_add_data_docs_sites,
-    ):
-        mock_context = MagicMock()
-        mock_get_context.return_value = mock_context
-
-        s3_bucket = "test-bucket"
-        namespace = "test-namespace"
-        key_prefix = "test-prefix"
-
-        # Call the function
-        result_context = run_gx_on_pq.create_context(s3_bucket, namespace, key_prefix)
-
-        # Assert that the context returned is the mock context
-        assert result_context == mock_context
-
-        # Assert that the other functions were called
-        mock_add_datasource.assert_called_once_with(mock_context)
-        mock_add_validation_stores.assert_called_once_with(
-            mock_context, s3_bucket, namespace, key_prefix
-        )
-        mock_add_data_docs_sites.assert_called_once_with(
-            mock_context, s3_bucket, namespace, key_prefix
+def spark_session():
+    yield pyspark.sql.SparkSession.builder.appName("BatchRequestTest").getOrCreate()
+
+
+@pytest.fixture()
+def cloudformation_bucket():
+    with mock_s3():
+        # Create a mock S3 client
+        s3 = boto3.client("s3")
+
+        # Define the bucket name
+        bucket_name = "test-great-expectations-bucket"
+
+        # Create the mock bucket
+        s3.create_bucket(Bucket=bucket_name)
+
+        # Create a sample great_expectations.yml with just the components we modify
+        great_expectations_content = """
+        config_version: 3.0
+        stores:
+            validations_store:
+                class_name: ValidationsStore
+                store_backend:
+                    class_name: TupleS3StoreBackend
+                    suppress_store_backend_id: true
+                    bucket: "{shareable_artifacts_bucket}"
+                    prefix: "{namespace}/great_expectation_reports/parquet/validations/"
+        data_docs_sites:
+            s3_site:
+                class_name: SiteBuilder
+                store_backend:
+                    class_name: TupleS3StoreBackend
+                    bucket: "{shareable_artifacts_bucket}"
+                    prefix: "{namespace}/great_expectation_reports/parquet/"
+                site_index_builder:
+                    class_name: DefaultSiteIndexBuilder
+        """
+
+        # Upload the great_expectations.yml file to the mocked bucket
+        s3.put_object(
+            Bucket=bucket_name,
+            Key="great_expectations.yml",
+            Body=great_expectations_content,
         )
 
+        # Yield the bucket name for use in tests
+        yield {
+            "bucket": bucket_name,
+            "great_expectations_configuration_key": "great_expectations.yml",
+            "great_expectations_content": great_expectations_content,
+        }
 
-def test_that_add_datasource_calls_correctly():
-    mock_context = MagicMock()
-    result_context = run_gx_on_pq.add_datasource(mock_context)
 
-    # Verify that the datasource was added
-    mock_context.add_datasource.assert_called_once()
-    assert result_context == mock_context
+@pytest.fixture()
+def clean_up_after_configure_gx_config():
+    """Remove artifacts of `configure_gx_config` function"""
+    yield
+    if os.path.isdir("gx"):
+        shutil.rmtree("gx")
 
 
-@pytest.mark.integration
-def test_that_add_datasource_adds_correctly(test_context):
-    # Assuming you've already added a datasource, you can list it
-    run_gx_on_pq.add_datasource(test_context)
-    datasources = test_context.list_datasources()
-
-    # Define the expected datasource name
-    expected_datasource_name = "spark_datasource"
-
-    # Check that the expected datasource is present and other details are correct
-    assert any(
-        ds["name"] == expected_datasource_name for ds in datasources
-    ), f"Datasource '{expected_datasource_name}' was not added correctly."
-    datasource = next(
-        ds for ds in datasources if ds["name"] == expected_datasource_name
+def test_configure_gx_config_validations_store_bucket(
+    cloudformation_bucket, clean_up_after_configure_gx_config
+):
+    gx_config = run_gx_on_pq.configure_gx_config(
+        gx_config_bucket=cloudformation_bucket["bucket"],
+        gx_config_key=cloudformation_bucket["great_expectations_configuration_key"],
+        shareable_artifacts_bucket="shareable_artifacts_bucket",
+        namespace="namespace",
+    )
+    assert (
+        gx_config["stores"]["validations_store"]["store_backend"]["bucket"]
+        == "shareable_artifacts_bucket"
     )
-    assert datasource["class_name"] == "Datasource"
-    assert "SparkDFExecutionEngine" in datasource["execution_engine"]["class_name"]
-
-
-def test_add_validation_stores_has_expected_calls():
-    mock_context = MagicMock()
-    s3_bucket = "test-bucket"
-    namespace = "test-namespace"
-    key_prefix = "test-prefix"
 
-    with patch.object(mock_context, "add_store") as mock_add_store:
-        # Call the function
-        result_context = run_gx_on_pq.add_validation_stores(
-            mock_context, s3_bucket, namespace, key_prefix
-        )
 
-        # Verify that the validation store is added
-        mock_add_store.assert_called_once_with(
-            "validation_result_store",
-            {
-                "class_name": "ValidationsStore",
-                "store_backend": {
-                    "class_name": "TupleS3StoreBackend",
-                    "bucket": s3_bucket,
-                    "prefix": f"{namespace}/{key_prefix}",
-                },
-            },
+def test_configure_gx_config_validations_store_prefix(
+    cloudformation_bucket, clean_up_after_configure_gx_config
+):
+    gx_config = run_gx_on_pq.configure_gx_config(
+        gx_config_bucket=cloudformation_bucket["bucket"],
+        gx_config_key=cloudformation_bucket["great_expectations_configuration_key"],
+        shareable_artifacts_bucket="shareable_artifacts_bucket",
+        namespace="namespace",
+    )
+    original_gx_config = yaml.safe_load(
+        cloudformation_bucket["great_expectations_content"]
+    )
+    # fmt: off
+    assert (
+        gx_config["stores"]["validations_store"]["store_backend"]["prefix"]
+        == original_gx_config["stores"]["validations_store"]["store_backend"]["prefix"].format(
+            namespace="namespace"
         )
-
-        assert result_context == mock_context
+    )
+    # fmt: on
 
 
-@pytest.mark.integration
-def test_validation_store_details(test_context):
-    # Mock context and stores
-    run_gx_on_pq.add_validation_stores(
-        test_context,
-        s3_bucket="test-bucket",
-        namespace="test",
-        key_prefix="test_folder/",
+def test_configure_gx_config_data_docs_sites_bucket(
+    cloudformation_bucket, clean_up_after_configure_gx_config
+):
+    gx_config = run_gx_on_pq.configure_gx_config(
+        gx_config_bucket=cloudformation_bucket["bucket"],
+        gx_config_key=cloudformation_bucket["great_expectations_configuration_key"],
+        shareable_artifacts_bucket="shareable_artifacts_bucket",
+        namespace="namespace",
+    )
+    original_gx_config = yaml.safe_load(
+        cloudformation_bucket["great_expectations_content"]
+    )
+    assert (
+        gx_config["data_docs_sites"]["s3_site"]["store_backend"]["bucket"]
+        == "shareable_artifacts_bucket"
     )
 
-    # Run the test logic
-    stores = test_context.list_stores()
-    expected_store_name = "validation_result_store"
-
-    assert any(store["name"] == expected_store_name for store in stores)
-    # pulls the store we want
-    store_config = [store for store in stores if store["name"] == expected_store_name][
-        0
-    ]
 
-    assert store_config["class_name"] == "ValidationsStore"
-    assert store_config["store_backend"]["class_name"] == "TupleS3StoreBackend"
-    assert store_config["store_backend"]["bucket"] == "test-bucket"
-    assert store_config["store_backend"]["prefix"] == "test/test_folder/"
+def test_configure_gx_config_data_docs_sites_prefix(
+    cloudformation_bucket, clean_up_after_configure_gx_config
+):
+    gx_config = run_gx_on_pq.configure_gx_config(
+        gx_config_bucket=cloudformation_bucket["bucket"],
+        gx_config_key=cloudformation_bucket["great_expectations_configuration_key"],
+        shareable_artifacts_bucket="shareable_artifacts_bucket",
+        namespace="namespace",
+    )
+    original_gx_config = yaml.safe_load(
+        cloudformation_bucket["great_expectations_content"]
+    )
+    # fmt: off
+    assert (
+        gx_config["data_docs_sites"]["s3_site"]["store_backend"]["prefix"]
+        == original_gx_config["data_docs_sites"]["s3_site"]["store_backend"]["prefix"].format(
+            namespace="namespace"
+        )
+    )
+    # fmt: on
 
 
 def test_get_spark_df_has_expected_calls():
-    glue_context = MagicMock()
-    mock_dynamic_frame = MagicMock()
-    mock_spark_df = MagicMock()
+    glue_context = unittest.mock.MagicMock()
+    mock_dynamic_frame = unittest.mock.MagicMock()
+    mock_spark_df = unittest.mock.MagicMock()
     mock_dynamic_frame.toDF.return_value = mock_spark_df
 
-    with patch.object(
+    with unittest.mock.patch.object(
         glue_context, "create_dynamic_frame_from_options"
     ) as mock_create_dynamic_frame:
         mock_create_dynamic_frame.return_value = mock_dynamic_frame
@@ -171,46 +188,14 @@ def test_get_spark_df_has_expected_calls():
         assert result_df == mock_spark_df
 
 
-def test_get_batch_request():
-    spark_dataset = MagicMock()
+def test_get_batch_request(gx_context):
+    spark_dataset = unittest.mock.MagicMock()
     data_type = "test-data"
-    run_id = RunIdentifier(run_name="2023_09_04")
-
-    batch_request = run_gx_on_pq.get_batch_request(spark_dataset, data_type, run_id)
-
-    # Verify the RuntimeBatchRequest is correctly set up
-    assert isinstance(batch_request, RuntimeBatchRequest)
-    assert batch_request.data_asset_name == f"{data_type}-parquet-data-asset"
-    assert batch_request.batch_identifiers == {
-        "batch_identifier": f"{data_type}_{run_id.run_name}_batch"
-    }
-    assert batch_request.runtime_parameters == {"batch_data": spark_dataset}
-
-
-@pytest.mark.integration
-def test_that_get_batch_request_details_are_correct(test_spark):
-    # Create a simple PySpark DataFrame to simulate the dataset
-    data = [("Alice", 34), ("Bob", 45), ("Charlie", 29)]
-    columns = ["name", "age"]
-    spark_dataset = test_spark.createDataFrame(data, columns)
-
-    # Create a RunIdentifier
-    run_id = RunIdentifier(run_name="test_run_2023")
-
-    # Call the function and get the RuntimeBatchRequest
-    data_type = "user_data"
-    batch_request = run_gx_on_pq.get_batch_request(spark_dataset, data_type, run_id)
-
-    # Assertions to check that the batch request is properly populated
-    assert isinstance(batch_request, RuntimeBatchRequest)
-    assert batch_request.datasource_name == "spark_datasource"
-    assert batch_request.data_connector_name == "runtime_data_connector"
-    assert batch_request.data_asset_name == "user_data-parquet-data-asset"
-    assert (
-        batch_request.batch_identifiers["batch_identifier"]
-        == "user_data_test_run_2023_batch"
+    batch_request = run_gx_on_pq.get_batch_request(
+        gx_context=gx_context, spark_dataset=spark_dataset, data_type=data_type
     )
-    assert batch_request.runtime_parameters["batch_data"] == spark_dataset
+    assert isinstance(batch_request, gx.datasource.fluent.batch_request.BatchRequest)
+    assert batch_request.data_asset_name == "test-data_spark_dataframe"
 
 
 def test_read_json_correctly_returns_expected_values():
@@ -218,13 +203,13 @@ def test_read_json_correctly_returns_expected_values():
     key = "test-key"
 
     # Mock the S3 response
-    mock_s3_response = MagicMock()
+    mock_s3_response = unittest.mock.MagicMock()
     mock_s3_response["Body"].read.return_value = '{"test_key": "test_value"}'.encode(
         "utf-8"
     )
 
     # Use patch to mock the boto3 s3 client
-    with patch("boto3.client") as mock_s3_client:
+    with unittest.mock.patch("boto3.client") as mock_s3_client:
         # Mock get_object method
         mock_s3_client.return_value.get_object.return_value = mock_s3_response
 
@@ -240,62 +225,25 @@ def test_read_json_correctly_returns_expected_values():
         assert result == {"test_key": "test_value"}
 
 
-def test_that_add_expectations_from_json_has_expected_call():
-    mock_context = MagicMock()
-
-    # Sample expectations data
+@pytest.mark.integration
+def test_add_expectations_from_json_adds_details_correctly(gx_context):
+    # Mock expectations data
     expectations_data = {
-        "test-data": {
-            "expectation_suite_name": "test_suite",
+        "user_data_one": {
+            "expectation_suite_name": "user_data_one_suite",
             "expectations": [
                 {
                     "expectation_type": "expect_column_to_exist",
-                    "kwargs": {"column": "test_column"},
+                    "kwargs": {"column": "user_id"},
                 },
-            ],
-        }
-    }
-
-    data_type = "test-data"
-
-    # Call the function
-    run_gx_on_pq.add_expectations_from_json(expectations_data, mock_context, data_type)
-
-    # Verify expectations were added to the context
-    mock_context.add_or_update_expectation_suite.assert_called_once()
-
-
-def test_that_add_expectations_from_json_throws_value_error():
-    mock_context = MagicMock()
-
-    # Sample expectations data
-    expectations_data = {
-        "not-test-data": {
-            "expectation_suite_name": "test_suite",
-            "expectations": [
                 {
-                    "expectation_type": "expect_column_to_exist",
-                    "kwargs": {"column": "test_column"},
+                    "expectation_type": "expect_column_values_to_be_between",
+                    "kwargs": {"column": "age", "min_value": 18, "max_value": 65},
                 },
             ],
-        }
-    }
-
-    data_type = "test-data"
-    with pytest.raises(
-        ValueError, match="No expectations found for data type 'test-data'"
-    ):
-        run_gx_on_pq.add_expectations_from_json(
-            expectations_data, mock_context, data_type
-        )
-
-
-@pytest.mark.integration
-def test_add_expectations_from_json_adds_details_correctly(test_context):
-    # Mock expectations data
-    expectations_data = {
-        "user_data": {
-            "expectation_suite_name": "user_data_suite",
+        },
+        "user_data_two": {
+            "expectation_suite_name": "user_data_two_suite",
             "expectations": [
                 {
                     "expectation_type": "expect_column_to_exist",
@@ -306,20 +254,25 @@ def test_add_expectations_from_json_adds_details_correctly(test_context):
                     "kwargs": {"column": "age", "min_value": 18, "max_value": 65},
                 },
             ],
-        }
+        },
     }
 
-    data_type = "user_data"
-
     # Call the function to add expectations
-    test_context = run_gx_on_pq.add_expectations_from_json(
-        expectations_data, test_context, data_type
+    run_gx_on_pq.add_expectations_from_json(
+        expectations_data=expectations_data, context=gx_context
     )
 
+    expectation_suites_in_store = [
+        suite.expectation_suite_name
+        for suite in gx_context.expectations_store.list_keys()
+    ]
+    assert "user_data_one_suite" in expectation_suites_in_store
+    assert "user_data_two_suite" in expectation_suites_in_store
+
     # Retrieve the expectation suite to verify that expectations were added
-    expectation_suite = test_context.get_expectation_suite("user_data_suite")
+    expectation_suite = gx_context.get_expectation_suite("user_data_one_suite")
 
-    assert expectation_suite.expectation_suite_name == "user_data_suite"
+    assert expectation_suite.expectation_suite_name == "user_data_one_suite"
     assert len(expectation_suite.expectations) == 2
 
     # Verify the details of the first expectation
@@ -335,47 +288,3 @@ def test_add_expectations_from_json_adds_details_correctly(test_context):
         "min_value": 18,
         "max_value": 65,
     }
-
-
-def test_that_add_validation_results_to_store_has_expected_calls():
-    # Mock the EphemeralDataContext and the necessary components
-    mock_context = MagicMock()
-    mock_expectation_suite = MagicMock()
-    mock_context.get_expectation_suite.return_value = mock_expectation_suite
-    mock_expectation_suite.expectation_suite_name = "test_suite"
-
-    # Mock the validation result data
-    validation_result = {"result": "test_result"}
-
-    # Create a mock batch identifier and run identifier
-    mock_batch_identifier = MagicMock(spec=RuntimeBatchRequest)
-    mock_run_identifier = MagicMock(spec=RunIdentifier)
-
-    # Call the function with mocked inputs
-    result_context = run_gx_on_pq.add_validation_results_to_store(
-        context=mock_context,
-        expectation_suite_name="test_suite",
-        validation_result=validation_result,
-        batch_identifier=mock_batch_identifier,
-        run_identifier=mock_run_identifier,
-    )
-
-    # Assert that the expectation suite was retrieved correctly
-    mock_context.get_expectation_suite.assert_called_once_with("test_suite")
-
-    expected_expectation_suite_identifier = ExpectationSuiteIdentifier(
-        expectation_suite_name="test_suite"
-    )
-    expected_validation_result_identifier = ValidationResultIdentifier(
-        expectation_suite_identifier=expected_expectation_suite_identifier,
-        batch_identifier=mock_batch_identifier,
-        run_id=mock_run_identifier,
-    )
-
-    # Verify that the validation result was added to the validations store
-    mock_context.validations_store.set.assert_called_once_with(
-        expected_validation_result_identifier, validation_result
-    )
-
-    # Check that the context is returned
-    assert result_context == mock_context