Uber Data Analyst candidates report recruiter and manager conversations followed by SQL, analytical cases, and, in some processes, a take-home or CSV analysis presentation. Prepare to explain your reasoning, not only the final answer.
$131K
Avg. Base Comp
$150K
Avg. Total Comp
3 rounds
Typical Rounds
2 months
Process Length
Uber Data Analyst interviews reported here place substantial weight on how you reason through an analysis. Candidates describe recruiter or HR conversations that cover background, motivation, teamwork, and communicating findings to non-technical stakeholders. Manager discussions may then test how you scope an ambiguous data problem, discuss prior projects, or explain the choices behind a take-home analysis.
Technical evidence repeatedly centers on SQL. Reported exercises include joins, grouped aggregations, HAVING filters, query efficiency, and window functions such as ranking, LEAD, and LAG. One candidate was given table schemas and expected input and output; another was asked to review SQL for mistakes. Practice narrating assumptions, edge cases, and tradeoffs as you write, and leave enough time to check the query against the requested output.
Several candidates also report product- or fraud-oriented cases, including choosing metrics for ride-cancellation fraud, investigating a metric drop after an experiment, and defining success for a feature or fraud-prevention launch. Structure these answers around the decision to be made, the segments to examine, possible confounders, and how you would communicate a recommendation. Some processes include a CSV or take-home presentation where interviewers probe data sources, exclusions, and analytical choices. Reported loops vary, so treat the exact sequence and duration as variable.
Synthesized from 9 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Uber process.
The assessment was the part that caught me off guard because it combined a lot of aptitude questions with SQL. I had a 60-minute test with 17 questions: 15 aptitude questions, which were fairly easy and could probably be finished in about 30 minutes, plus two medium-level SQL problems. There were also a few basic ML-related questions. I spent most of my time on the SQL and wasn’t able to fully solve it, so the time pressure mattered much more there than on the aptitude section.
For candidates who moved forward, the interview itself was structured as two roughly 30-minute rounds. The technical round stayed focused on SQL, with two easy-to-moderate practical business questions. They tested aggregates, grouping, filtering grouped results with HAVING, and whether I could write an efficient query rather than just get an answer. The second round was an HR conversation covering standard behavioral and general HR questions. The overall process felt smooth and organized, with an easy-to-moderate level of difficulty. My main takeaway is to move quickly through the aptitude portion and reserve enough time to work through the SQL carefully, especially grouped aggregations and HAVING-based filters.
Prep tip from this candidate
Practice completing two medium SQL business-style questions under a tight time limit, especially aggregates, GROUP BY, HAVING, and query efficiency. The initial test also includes 15 relatively easy aptitude questions plus some basic ML material, so avoid spending too long on those.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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 | |
| Button AB Test | |
| Weighted Keys | |
| Top 3 Users | |
| Third Purchase | |
| Maximum Profit | |
| Bank Fraud Model | |
| Encoding Categorical Features | |
| Network Experiment Design | |
| Distance Traveled | |
| Revenue Retention | |
| P-value to a Layman | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Random Forest Explanation | |
| Type-ahead Search | |
| Sort Strings | |
| WAU vs Open Rates | |
| Uber User Journey | |
| Hurdles In Data Projects | |
| Bloated Mid-Funnel | |
| Xgboost vs Random Forest | |
| Production Model Monitoring | |
| Dijkstra implementation | |
| Uniform Car Maker | |
| Assumptions of Linear Regression | |
| Testing Price Increase |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR screen covering background, motivation, teamwork, salary expectations, problem solving, and examples of communicating analysis. Some also describe questions about explaining findings to non-technical stakeholders or using data to address a client problem.
Candidates report manager conversations that may combine competency questions with prior-project discussion. Prompts included how to approach an ambiguous problem with data, how to optimize a process, and how to explain the reasoning behind an analysis or presentation.
Technical content reported includes SQL joins, grouped aggregations, HAVING, query review, ranking, LEAD, and LAG. Candidates also describe fraud-metric, experiment-diagnosis, conversion, and product-success cases; some processes included a CSV or take-home analysis presented to interviewers.
