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Welcome Backend-focused Product Exercise

TIMEBOX:    2-3 hours max. We mean it! Set at timer and hard-stop at 3 hours ⏱
LANGUAGES:  Python, Javascript, SQL
FRAMEWORKS: Django, Flask, NodeJS, etc (Exercise B can be pure SQL)
TESTS:      nice to have, but not mandatory
DOCS:       nice to have, but not mandatory

Overview

This exercise is to implement the best possible solution to one of the exercises below in the time alloted. We're evaluating your ability to take a set of requirements and spike a holistic solution that demonstrates craftsmanship, thoughtfulness and good architectural design. This is NOT a test of how well you know Python/Django/Flask/SQL, nor should you try to impress us with overly clever and obtuse solutions. If you want to impress us, build something that is beautiful, intuitive and easy to debug/test/extend 😃 .

Ideally your solution would have some way to run locally and test the results so we can fully analyze your efforts.

Choose One Of the following

🚨 ❗ 👉 Please use one of the salary datasets provided here for either exercise /shared/salary_datasets 👈 ❗ 🚨

Exercise A: Expose an API for querying compensation data

The goal of this exercise is to design a read-only API (REST or GraphAPI) that returns one or more records from static set of compensation data.

User Story: As a developer I want to

  • list compensation data via API GET request
    • Filter by one or more fields/attributes (e.g. /compensation_data?salary[gte]=120000&zip_code=11201 )
    • Sort by one or more fields/attributes (e.g. /compensation_data?sort=last_name)
  • fetch a single record via GET request
    • Stretch Goal: return a sparse fieldset (e.g. /compensation_data?fields=first_name,last_name,salary)
  • have the JSON response be normalized into a uniform schema via a serializer or json template
    • Stretch Goal: serialize more than one compensation data set

A few quick notes on submitting Exercise A

  • Don't worry about any web application concerns other than serializing JSON and returning via a GET request.
  • The example above (/compensation_data...) is not a contract. Feel free to design the URL structure and JSON schema that you believe creates the best client/consumer experience

Exercise B: Database Design Exercise - Storing Compensation Data

The goal of this exercise is to design a highly normalized database schema for storing compensation data

  • Create a schema for storing the compensation data provided in one of the available data sets. This schema should be in at least 3NF with tables for employee, role, and anything else that makes sense for the data given.
  • Upload at least one dataset to the schema
    • Stretch Goal: upload all 3 salary datasets to the database schema
  • Validate that you can perform the following queries. You can export the results of these queries via CSV or attach screenshots of the the output
    • Average compensation of roles where the role is some kind of engineer (hint: ILIKE)
    • Average, min, and max compensation per city (if available in dataset)
    • One interesting query of your choice (average compensation by gender perhaps???)
  • Create a quick database schema diagram
    • Many admin tools and clients will allow you to generate these from your schema. If not possible, just draw a super-simple diagram in MS Paint or similar tool 🎨

A few quick notes on submitting Exercise B

  • Ideally this exercise would use SQLite or Postgres, but any SQL database is OK
  • Feel free to upload the entire SQL dump (with schema) of the populated database, or create a script that creates the schema and populates the database with one or more of the provided salary data CSVs. Please do whatever makes the most sense given the time alloted.
  • If you'd like to use a scripting language like Python or Ruby along with an ORM to make this easier, thats fine with us!

Submitting your exercise

  1. See instructions for submitting your work