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.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Uber process.
The process had four stages, and each one tested a slightly different skill. It started with a one-hour online assessment covering logical reasoning, English, and some SQL questions. I found the timing tight, so I'd recommend practicing SQL under a clock before you sit it. Next was a take-home with a handful of SQL tasks and a code review exercise. The review part asked me to read a piece of existing work, find the mistakes, and fix them. That was the piece that required the most care, because you have to understand someone else's logic before you can judge it.
The live coding session came next, with one of the senior members of the team. They opened with a few technical questions to see how I reasoned about data work, and then asked me to write a single SQL query while I talked through my approach. The interviewer cared more about how I structured the query and explained my choices than about producing a perfect answer on the first try.
The last step was a 30-minute meeting with my potential manager. It was less technical and focused on how I handle different work situations and how I communicate. I came out of it feeling the conversation had been more of a discussion than a quiz.
I received an offer at the end of the process. If you're preparing, spend real time on SQL, but also practice reviewing queries you didn't write, because that exercise is easy to underestimate.
Prep tip from this candidate
Practice finding and fixing bugs in SQL written by someone else, since the take-home included a code review task, and drill SQL under timed conditions for the one-hour online assessment.
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 | |
| 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.
