diff --git a/main.py b/main.py
index d8b5d07..3721aed 100644
--- a/main.py
+++ b/main.py
@@ -1,80 +1,22 @@
 """ETL script for raw Epi/HAI CRE alert docx."""
 
 import io
-import sys
-from pathlib import Path
 
-import boto3 as boto3
 import dateutil.parser as dparser
 import pandas as pd
-from awsglue.context import GlueContext
-from awsglue.utils import getResolvedOptions
+from capepy.aws.glue import EtlJob
 from docx import Document
-from pyspark.sql import SparkSession
 
-# for our purposes here, the spark and glue context are only (currently) needed
-# to get the logger.
-spark_ctx = SparkSession.builder.getOrCreate()  # pyright: ignore
-glue_ctx = GlueContext(spark_ctx)
-logger = glue_ctx.get_logger()
-
-# TODO:
-#   - add error handling for the format of the document being incorrect
-#   - figure out how we want to name and namespace clean files (e.g. will we
-#     take the object key we're given, strip the extension and replace it with
-#     one for the new format, or will we do something else)
-#   - see what we can extract out of here to be useful for other ETLs. imagine
-#     we'd have a few different things that could be made into a reusable
-#     package
-
-parameters = getResolvedOptions(
-    sys.argv,
-    [
-        "RAW_BUCKET_NAME",
-        "ALERT_OBJ_KEY",
-        "CLEAN_BUCKET_NAME",
-    ],
-)
-
-raw_bucket_name = parameters["RAW_BUCKET_NAME"]
-alert_obj_key = parameters["ALERT_OBJ_KEY"]
-clean_bucket_name = parameters["CLEAN_BUCKET_NAME"]
+etl_job = EtlJob()
 
 # NOTE: for now we'll take the alert object key and change out the file
 #       extension for the clean data (leaving all namespacing and such). this
 #       will probably need to change
-clean_obj_key = str(Path(alert_obj_key).with_suffix(".csv"))
-
-# NOTE: May need some creds here
-s3_client = boto3.client("s3")
-
-# try to get the docx object from S3 and handle any error that would keep us
-# from continuing.
-response = s3_client.get_object(Bucket=raw_bucket_name, Key=alert_obj_key)
-
-status = response.get("ResponseMetadata", {}).get("HTTPStatusCode")
-
-if status != 200:
-    err = (
-        f"ERROR - Could not get object {alert_obj_key} from bucket "
-        f"{raw_bucket_name}. ETL Cannot continue."
-    )
-
-    logger.error(err)
-
-    # NOTE: need to properly handle exception stuff here, and we probably want
-    #       this going somewhere very visible (e.g. SNS topic or a perpetual log
-    #       as someone will need to be made aware)
-    raise Exception(err)
-
-logger.info(f"Obtained object {alert_obj_key} from bucket {raw_bucket_name}.")
-
-# handle the document itself...
+clean_obj_key = etl_job.parameters["OBJECT_KEY"].replace(".docx", ".csv")
 
 # the response should contain a StreamingBody object that needs to be converted
 # to a file like object to make the docx library happy
-f = io.BytesIO(response.get("Body").read())
-document = Document(f)
+document = Document(io.BytesIO(etl_job.get_raw_file()))
 
 # NOTE: this document is assumed to contain a single table that needs to be
 #       processed and nothing else. The file consists of:
@@ -125,27 +67,4 @@
 # write out the transformed data
 with io.StringIO() as csv_buff:
     interim.to_csv(csv_buff, index=False)
-
-    response = s3_client.put_object(
-        Bucket=clean_bucket_name, Key=clean_obj_key, Body=csv_buff.getvalue()
-    )
-
-    status = response.get("ResponseMetadata", {}).get("HTTPStatusCode")
-
-    if status != 200:
-        err = (
-            f"ERROR - Could not write transformed data object {clean_obj_key} "
-            f"to bucket {clean_bucket_name}. ETL Cannot continue."
-        )
-
-        logger.error(err)
-
-        # NOTE: need to properly handle exception stuff here, and we probably
-        #       want this going somewhere very visible (e.g. SNS topic or a
-        #       perpetual log as someone will need to be made aware)
-        raise Exception(err)
-
-    logger.info(
-        f"Transformed {raw_bucket_name}/{alert_obj_key} and wrote result "
-        f"to {clean_bucket_name}/{clean_obj_key}"
-    )
+    etl_job.write_clean_file(csv_buff.getvalue(), clean_obj_key)
diff --git a/requirements.txt b/requirements.txt
index fe0f945..972b427 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 aws-glue-libs @ git+https://github.com/awslabs/aws-glue-libs@9d8293962e6ffc607e5dc328e246f40b24010fa8
 boto3==1.34.103
+capepy>=1.0.0,<2.0.0
 openpyxl==3.1.2
 pandas==2.2.2
 pyspark==3.5.1