Data Analyst interview
at Uber.

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 you turned an ambiguous business question from operations into a concrete analysis that changed a decision.
2. 02 Describe a time you were customer obsessed and surfaced an insight about rider or driver behavior that others had missed.
3. 03 Give an example of when you had to defend your metric definition or methodology to a skeptical stakeholder.
4. 04 Tell me about a time you owned the full analysis lifecycle, from clarifying the question to presenting recommendations to leadership.

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

1. 01 Write a SQL query using window functions to return the third transaction of every user, outputting user id, spend, and transaction date.
2. 02 How would you calculate rider retention by signup cohort over the first 90 days, and how do you define an active rider?
3. 03 Trip completion rate dropped 5% in one city last week; walk me through the SQL and the diagnostic steps you'd use to find the cause.
4. 04 Using a trips table partitioned by city and time, write a query to identify the hours and zones with the worst driver-to-request imbalance.

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

1. 01 Design a dashboard and underlying data model that lets city operations managers monitor real-time marketplace health metrics.
2. 02 Design a reporting pipeline that tracks driver utilization and surge frequency across cities and refreshes on a reliable cadence.
3. 03 Design a cohort-based retention analytics dataset for riders that supports slicing by city, signup channel, and first-trip type.
4. 04 Design the metric definitions and data validation checks for a trip funnel report so that completion and cancellation numbers are trustworthy.