Starting as a data engineer means inheriting fragile ETL pipelines. New hires face frequent schema shifts, silent data corruption, and undocumented logic. The article argues that an automated test suite can surface these faults before they reach production.

The workflow relies on Docker, VS Code, and the Dev Containers extension. By spinning up isolated containers, developers can run integration tests against mock databases and orchestration engines in a repeatable environment.

Testing first clarifies expected behavior. For example, a function that standardizes column names is validated against sample data, ensuring downstream tables receive clean, consistent fields. The article also highlights AI‑assisted code generation as a productivity boost.

In practice, setting up the container, writing unit tests, and running them locally catches upstream schema changes and data quality issues early, preventing costly downstream failures.