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.
HardSystem Design
0 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
0 practiced
Discuss the CAP theorem and apply it to a distributed title/catalog metadata service at Netflix. For different read/write paths (search, browsing, licensing operations), argue when to prioritize availability over consistency and propose mitigation strategies for inconsistency (versioning, conflict resolution, eventual reconciliation).
EasyTechnical
0 practiced
Compare and contrast the responsibilities of a Data Engineer, Data Scientist, and ML Engineer at a company like Netflix. Provide examples of deliverables that are unique to the Data Engineer role (e.g., production data pipelines, platform APIs, monitoring SLAs) and describe typical collaboration patterns with product, research, and infra teams.
EasyTechnical
0 practiced
SQL task: Given these tables, write a PostgreSQL query to compute average watch time per title over the last 30 days.Tables:- playback_events(event_id PK, user_id, content_id, event_type TEXT CHECK (event_type IN ('play','pause','stop')), event_time TIMESTAMP, position_seconds INTEGER)- content(title_id PK, title TEXT)Assume 'stop' indicates session end and position_seconds records the playback position at that event. Explain assumptions about missing events and overlapping sessions.
MediumTechnical
0 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.
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