Uber Data Analyst candidates report a recruiter screen, SQL testing, a case or take-home, and hiring-manager conversations. Reported process lengths range from about two to eight weeks, and the number of rounds varies.
$130K
Avg. Base Comp
$150K
Avg. Total Comp
3-8 rounds
Typical Rounds
2-8 weeks
Process Length
Candidates describe the Uber Data Analyst interview as SQL plus applied business reasoning. Most accounts begin with a recruiter or HR screen, and SQL shows up in nearly every report, from timed assessments to live queries. One candidate was asked for the top-spending client in the previous quarter. Another described joins, window functions such as ranking, LEAD and LAG, and grouped filtering with HAVING.
The second theme is defending your analysis. Several candidates report a take-home or CSV exercise followed by a presentation to a hiring manager. One was pushed on how the data was sourced and why other metrics were excluded. Another was asked how often they had used specific data tools in practice.
Product and experiment cases also come up. Candidates mention investigating a drop in rider booking conversion, designing an A/B test with guardrail metrics, finding the root cause of a metric drop after an experiment launch, and measuring fraud or feature success. A candidate who received an offer described a code-review task in which they had to find and fix mistakes in someone else's SQL.
Structure varies. Candidates report anywhere from three main rounds to eight interviews, and end-to-end timing ranges from about two weeks to about two months. Outcomes are mixed, with offers and rejections both reported. Some rejections came after the final round, and one candidate described an eligibility policy issue and another described scheduling and recruiter-handoff problems. Confirm logistics and eligibility early, practice SQL under time pressure, and prepare a clear story about one analysis you can explain from data source to business impact.
Synthesized from 14 candidate reports by our editorial team.
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Featured question at Uber
Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
| Question | |
|---|---|
| Experiment Validity | |
| Download Facts | |
| User Experience Percentage | |
| Weighted Keys | |
| Button AB Test | |
| Top 3 Users | |
| Third Purchase | |
| Maximum Profit | |
| Bank Fraud Model | |
| Encoding Categorical Features | |
| Network Experiment Design | |
| Distance Traveled | |
| Revenue Retention | |
| Size of Joins | |
| P-value to a Layman | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Random Forest Explanation | |
| Type-ahead Search | |
| One Million Rides | |
| Uber User Journey | |
| WAU vs Open Rates | |
| Sort Strings | |
| Binary Tree Conversion | |
| Bloated Mid-Funnel | |
| Xgboost vs Random Forest | |
| Production Model Monitoring | |
| Hurdles In Data Projects | |
| Dijkstra implementation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter or HR call covering background, motivation, and behavioral topics such as teamwork, communicating findings to non-technical audiences, or a process you improved. Some also mention salary expectations or a check on SQL and Excel comfort. It is typically conversational, so prepare concise project examples.
Several candidates report a SQL-focused screen. One described a 60-minute test with aptitude questions and two medium SQL problems, and another a one-hour assessment with logical reasoning, English and SQL. Others mention a separate SQL test covering joins and window functions. Formats appear to vary, so practice timed grouped aggregations, joins and window functions.
Some candidates report a take-home task, such as a CSV analysis, a case study turned into a presentation, SQL tasks with a code-review exercise, or a one-day assignment. It may not appear in every loop. Be ready to explain how you sourced data, chose metrics, excluded alternatives and reached a recommendation.
Candidates report hiring-manager conversations that revisit their projects or take-home work, with behavioral questions. Technical rounds reportedly included live SQL, sometimes with pandas, and one mentioned database conflicts or upgrades. Expect to explain your reasoning aloud, not just produce a final query.
Later rounds reported by some candidates include product cases such as a rider booking-conversion drop, A/B test design with guardrail metrics, fraud metrics, metric-drop root-cause analysis, and measuring launch success. Some candidates also met senior team members or leaders from other teams. Practice structuring your approach and stating assumptions.
