Data Analyst interview
at Netflix.

Tell-me-about-a-time stories on ownership, conflict, and impact — scored against this company's real values.

1. 01 Tell me about a time your analysis led you to recommend against a popular feature, and how you delivered that conclusion with candor.
2. 02 Describe a situation where you took full ownership of an ambiguous analytics problem under 'freedom and responsibility' without being told how to solve it.
3. 03 Walk me through an insight you got wrong, how you caught it, and what you changed in your process afterward.
4. 04 Tell me about a time you influenced a senior stakeholder's decision purely through how you framed the data.

The hard screen for the craft itself — talked through out loud, not whiteboarded in your head.

1. 01 Write a window-function query to compute rolling 7-day and 28-day member retention and flag anomalies in content consumption.
2. 02 Write SQL to find members who watched more than five titles within a single genre over the last month using CTEs and efficient joins.
3. 03 Given an A/B test result table, write the query and reasoning to decide whether a feature change had a statistically meaningful effect.
4. 04 How would you define and compute a retention or engagement metric for a new 'Top 10' row, and what edge cases would you handle in the SQL?

Open-ended design of the systems this company actually runs, with the interviewer probing your tradeoffs.

1. 01 Design the dashboard and metric layer that lets product teams self-serve engagement and retention analytics across Netflix titles.
2. 02 Design the analytics data model for tracking the member viewing funnel from impression to play-start to completion.
3. 03 Design an experiment-readout pipeline that turns raw A/B test logs into trustworthy, decision-ready metrics.
4. 04 Design a content-performance reporting system that measures how a new original title drives engagement and retention by region.