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
As Netflix scales to thousands of datasets and hundreds of teams, propose a practical data governance model that balances self-service analytics with control: dataset ownership and certification, automated lineage checks, policy-as-code for access, cost accountability, and lightweight approval workflows. Explain enforcement mechanisms and cultural adoption strategies.
MediumSystem Design
0 practiced
Design a real-time ingestion pipeline to collect and process client playback events at peak load up to 500k events/sec for a streaming service. Requirements: end-to-end latency <5s for key metrics, durable storage for replay, schema validation, partitioning strategy for scaling, ability to reprocess historical events, and fault tolerance across regions. Sketch components, technology options (e.g., Kafka/Kinesis, stream processors), and key trade-offs.
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.
MediumSystem Design
0 practiced
Design an ETL process to refresh daily content metadata (titles, cast, genres) with zero downtime for downstream analytics and ML feature generation. Requirements: support partial incremental updates, atomic swap of production tables, data quality checks, backward compatibility for consumers, and minimal compute cost. Describe staging, validation, and deployment steps.
EasyTechnical
0 practiced
Explain the differences between a data lake and a data warehouse and provide concrete examples of how Netflix might use each: e.g., raw event lake for ingestion and reprocessing, curated warehouse for business reporting, and specialized stores for ML features. Discuss patterns for layering (bronze/silver/gold) and cost/performance trade-offs.
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