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.
HardTechnical
0 practiced
Given a large user-item interactions dataset stored as Parquet on S3, outline a scalable PySpark pseudocode job to compute a time-decayed popularity feature per item (exponential decay with half-life parameter). Explain partitioning strategy, avoidance of data skew, and how to perform incremental updates.
EasyTechnical
0 practiced
Name key privacy and compliance constraints (for example GDPR, CCPA) that affect how Netflix collects, stores, and uses user data. As an AI Engineer, how would you implement data minimization, access control, and auditability in ML pipelines?
EasyTechnical
0 practiced
Given this simplified playback event schema, identify which fields are critical for downstream recommendation features and which are optional. Then describe one transformation you'd perform to derive an engagement score per session.| field | type | example ||---|---:|---|| user_id | string | u_12345 || session_id | string | s_abc || event_type | string | play/pause/stop || timestamp | timestamp | 2025-05-01T12:34:56Z || playback_position | int | 125 || device_type | string | tv || content_id | string | movie_678 |
MediumSystem Design
0 practiced
Design a data pipeline to ingest and process 1 billion playback events per day into features used for offline training and for a near-real-time ranking feature with 30-minute freshness. Include components (ingest, stream/batch processing, storage formats), approximate throughput expectations, and failure-handling strategies.
EasyTechnical
0 practiced
As an AI Engineer, how would you explain model performance trade-offs (precision vs recall, latency vs accuracy, offline metric vs online business impact) to non-technical product managers at Netflix? Provide a concrete example and what visuals/metrics you would prepare.
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