Netflix
Mid
Netflix Software Engineer 5 — build the real-time event pipelines behind Ads measurement and billing
This role is on Netflix's Ads Reporting Infrastructure team, building the low-latency, high-scale pipelines that ingest billions of daily ad events from client/server/third-party sources and turn them into clean, privacy-safe streams for measurement, serving, reporting, and finance. The JD demands hands-on depth in JVM (or C++), stream processing (Apache Flink-class systems), event-driven pipeline design, and real ad-tech domain knowledge (SSP/DSP, pacing and budgeting, programmatic). Despite the numeric title, this reads as a senior IC role given the depth and scale bar — Netflix interviews skew heavily toward deep technical discussion and past-work specificity over trivia.
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What this interview tests
- Low-latency, high-throughput event ingestion pipeline design
- Stream processing frameworks (Apache Flink or equivalent), state and windowing
- Data accuracy/completeness monitoring and alerting at scale
- Ad tech domain depth: SSP/DSP, ad serving, pacing and budgeting, programmatic
- JVM (or C++) systems programming
- Distributed caching and real-time processing tradeoffs
Common question themes
Design a pipeline ingesting millions+ ad events/sec with correctness guarantees
How would you handle exactly-once vs at-least-once semantics in a Flink-style pipeline
Tell me about a data accuracy or completeness bug that had financial/business impact
Describe your experience with pacing/budgeting or ad-serving systems specifically
How do you design monitoring and alerting to catch silent data loss
Walk through a distributed caching or real-time system design decision you made
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