A senior data engineer inherits a fintech with 412 production DAGs
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 senior data engineer inherits a fintech with 412 production DAGs. Snowflake credit usage grew 3.5x year-over-year, on-call gets paged 8-12 times per night, three teams compute weekly active users with three different numbers, and an upstream cadence change broke the executive close last month. Apply the entire L4 advanced tier on this canvas: (a-s0) add a catalog node so asset lineage is queryable; (a-s1) make every transform backfill-ready by adding an orchestrator and replacing plain warehouse destinations with lakehouse formats; (a-s2) add an observability_tool + alert_destination on the customer-facing pipelines; (a-s3) split into at least 2 orchestrators for priority isolation between customer-facing and backfill workloads; (a-s4) replace cross-DAG time offsets with lakehouse asset boundaries.
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