Lyft
Mid
Data Engineer, Safety & Customer Care (SCC) — Lyft
Own the data pipelines and models behind Lyft's Safety & Customer Care Associate and AI Agent Platform, which handles 1.7M+ monthly human and AI support interactions. This role is squarely about scaling ETL, evolving data schemas, and tuning Spark/SQL performance for a high-growth, cost-sensitive data platform — not generic analytics support.
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What this interview tests
- Large-scale ETL pipeline ownership and scaling
- Spark/SQL performance and job tuning
- Data modeling and schema evolution
- Data quality/consistency systems
- Workflow orchestration (Airflow)
- Cross-functional translation of business asks into pipeline work
Common question themes
Walk through a pipeline you owned end-to-end and how you scaled it as data grew
Debug and tune a slow Spark or SQL job — what would you check first
How do you evolve a data schema without breaking downstream consumers
Design a data quality/consistency monitoring system for a pipeline
How would you build self-service ETL tooling for other teams
Tell me about a time you translated a vague business ask into a concrete data engineering solution
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