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

40 practiced

Discuss evaluation strategies to measure recommender performance specifically on long-tail content, where popular titles dominate metrics. Propose sampling methods, weighting schemes, and offline/online evaluation approaches to ensure long-tail performance is visible and optimized.

HardTechnical

43 practiced

Write a PostgreSQL-compatible SQL query that identifies 'binge sessions' from playback_events with schema:

sql

playback_events(
  event_id TEXT,
  user_id TEXT,
  title_id TEXT,
  start_ts TIMESTAMP,
  end_ts TIMESTAMP
)

Define a binge session as a sequence of plays by the same user where the gap between consecutive play end and next play start is <= 30 minutes, and the session must include 3+ distinct titles/episodes. Return session_id (user_id + session_number), user_id, session_start, session_end, titles_in_session.

MediumTechnical

35 practiced

Design an A/B experiment to evaluate a new personalization algorithm targeted at 'less-engaged' subscribers. Include: how you define the eligibility criteria, randomization strategy, power analysis variables, guardrail metrics to protect other segments, and post-hoc analysis to understand heterogeneous treatment effects.

HardTechnical

42 practiced

Storage costs have grown significantly due to many intermediate and duplicate artifacts in the data lake. Propose a concrete strategy to reduce data platform costs while preserving availability for analytics and ML workflows. Include lifecycle policies, compaction, deduplication, metadata/catalog improvements, and organizational policies.

EasyTechnical

41 practiced

Compare and contrast the responsibilities of Data Engineers and Data Scientists at a company like Netflix. Include examples around pipeline ownership, data modeling, experiment instrumentation, model deployment, and ongoing maintenance. How do these roles collaborate to deliver ML-driven product features?

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