A producer team renamed customer_id to user_id last week, dropped a required column, and started sen
A medium Pipeline Design mock interview question on DataDriven. Practice with AI-powered feedback, real code execution, and a hire/no-hire decision.
- Domain
- Pipeline Design
- Difficulty
- medium
Interview Prompt
A producer team renamed customer_id to user_id last week, dropped a required column, and started sending strings where numbers were expected. The pipeline silently absorbed all three changes; downstream joins and aggregates are now wrong in ways nobody has noticed. The section's pattern is schema validation: assert column exists, type matches, nullability respected, value-in-declared-range. Tools that can author and run these assertions include Great Expectations, dbt tests, and Soda, all of which the team can adopt incrementally. Validate the schema by adding a schema-validation check between the source and the curated table whose name lists what it asserts (existence, types, nullability, ranges).
How This Interview Works
- Read the vague prompt (just like a real interview)
- Ask clarifying questions to the AI interviewer
- Write your pipeline design solution with real code execution
- Get instant feedback and a hire/no-hire decision