Netflix
Senior
L5 Data Engineer building Netflix's Ads data ecosystem at scale
This role is on Netflix's Ads Data Engineering team, owning core data products around ad inventory, forecasting, targeting, ad serving, and pacing. Netflix's ads business is new and fast-evolving, so expect deep-dive questions on distributed data pipelines (Spark, Flink, Hive/Hadoop), data modeling across systems, and how candidates operate with high autonomy and taste for clean, simple code.
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What this interview tests
- Distributed data pipeline design (Spark, Flink, Hive/Hadoop)
- Data modeling and warehousing across multiple source systems
- Ownership and self-direction in ambiguous, fast-changing contexts
- Data governance for sensitive/advertising data (privacy, GDPR)
- Cross-functional partnership with Analytics Engineers, Data Scientists, ML
Common question themes
Architect a data product for ad inventory or targeting from multiple sources
Describe a large-scale batch or streaming pipeline you owned end-to-end
How you govern datasets containing sensitive advertising or user data
Tell me about a time you had to self-direct with minimal guidance
Tradeoffs in choosing Spark vs Flink vs Hive for a given workload
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