Netflix Business Context & Data Engineering Role Questions
Understanding Netflix's business model, product strategy, and organizational context, with a focus on the Data Engineering role. Covers how Netflix operates in streaming, content recommendations, data platforms, and data engineering responsibilities, including data pipelines, platform architecture, and how business goals drive data work within Netflix.
MediumTechnical
38 practiced
SQL: Write an ANSI SQL / PostgreSQL query to compute 7-day retention cohorts. For each signup_date, compute the percentage of users who returned and streamed at least 60 seconds on day 7 after signup.Tables:- users(user_id, signup_date DATE)- playback_events(user_id, occurred_at TIMESTAMP, duration_seconds INT)Explain any timezone assumptions and how you handle missing or sparse data.
MediumTechnical
46 practiced
You discover an upstream bug that corrupted playback metrics for the last 90 days. Design a backfill strategy to rebuild affected datasets while minimizing disruption to analysts and ensuring consistent historical aggregates. Include dependency ordering, incremental reprocessing plan, verification queries, and a communication plan.
HardSystem Design
50 practiced
Design a migration strategy to move feature generation from a batch-only pipeline to a hybrid model that supports both precomputed batch features and near-real-time streaming features for recommendations. Cover consistency between offline and online features, feature lineage/versioning, testing and validation, canary rollout, and operational complexity.
HardTechnical
34 practiced
Scenario: You detect intermittent data corruption where certain daily aggregates are unexpectedly 20% higher overnight. Design a root cause analysis (RCA) process: immediate mitigation (isolate jobs, switch to snapshot), checkpoints and comparisons to identify divergence, rollback or backfill plan, long-term fixes (tests, schema checks), and communication plan to stakeholders.
MediumSystem Design
43 practiced
Design a privacy-preserving analytics pipeline that enables Netflix researchers to run aggregated analyses while protecting PII. Describe approaches such as cohort size thresholds, differential privacy (noise addition), k-anonymity pitfalls, pseudonymization, secure enclaves for sensitive joins, and audit controls. Explain how these integrate into ETL and access control.
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