DoorDash Data Analyst candidates report a recruiter screen, a case study (take-home in some accounts), SQL rounds and behavioral conversations. Prepare for window-function SQL, metric definition and experiment design.
$129K
Avg. Base Comp
$170K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describing DoorDash Data Analyst interviews point to two recurring themes: SQL under time pressure and open-ended business cases. Several accounts mention a recruiter conversation first, then a case component, then SQL and behavioral interviews, though the order and format differ from person to person.
On the SQL side, one candidate reports four questions on order-frequency segmentation and restaurant sales trends. They moved from grouping and counting to month-over-month calculations and quartiles, including interpreting NTILE(4) and editing existing code. Another describes a round covering GROUP BY, CTEs and LAG. A third was shown a long CTE query and asked whether it correctly produced month-over-month order details. Window functions and month-over-month logic therefore deserve focused practice, along with checking your edge cases. One candidate also found the SQL prompts ambiguous, so say your assumptions out loud.
The case work looks like the heaviest part. One candidate reports a take-home that took well over 10 hours. It asked them to explain a difference in customer complaints between two fulfillment setups and to make recommendations from the data. Another reports a take-home returned within about 48 hours, which was later reviewed live with the hiring manager. That case asked for success metrics, experiment design and diagnostics. A third describes a conversational case discussion with many follow-up questions built on their answers.
Behavioral questions appear alongside the technical work. They covered career history and cross-functional collaboration, and one was an unexpected prompt about a "chip on your shoulder". All the Data Analyst candidates here who reported an outcome did not receive an offer, so the sample is small. Structure your analysis clearly, tie it to business decisions and be ready to defend your reasoning.
Synthesized from 7 candidate reports by our editorial team.
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Real interview reports from people who went through the Doordash process.
The technical round consisted of four SQL questions on customer order-frequency segmentation and restaurant sales-trend analysis. The questions increased in complexity, from grouping and counting to multi-step month-over-month calculations and quartile distributions. Time pressure was the main challenge, leaving little time to double-check logic or edge cases.
One question involved interpreting NTILE(4). I was also given existing SQL code and asked to update it.
Prep tip from this candidate
Practice timed SQL drills, including grouping, month-over-month calculations, quartiles, and editing an existing query. Leave time to validate edge cases.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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| Question | |
|---|---|
| Monthly Customer Report | |
| Experiment Validity | |
| Month Over Month | |
| Button AB Test | |
| Average Order Value | |
| Over-Budget Projects | |
| Longest Streak Users | |
| Network Experiment Design | |
| Daily Retention Summary | |
| Instagram TV Success | |
| Delivery Estimate Model | |
| Group Success | |
| Post Composer Drop | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Uber User Journey | |
| Comparing Search Engines | |
| Netflix Retention | |
| Marketing Channel Metrics | |
| WAU vs Open Rates | |
| Biggest Tip | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Recruiting Leads | |
| Forecasting New Year Revenue | |
| Food Delivery Times | |
| Random Bucketing | |
| Success Measurement | |
| Testing Price Increase |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a conversational recruiter call that typically covers your resume and sets out the next steps. One candidate also mentions a SQL overview early in the process. Be ready to summarize your analytics experience and your SQL background clearly.
Candidates report a case study that may be done offline. One account describes a return window of about 48 hours. Another says the work took well over 10 hours. Prompts reportedly involve explaining operational differences, recommending actions and defining metrics. Plan your time and keep your narrative clear.
Candidates report SQL questions on GROUP BY, CTEs, LAG and LEAD, month-over-month calculations, quartiles and NTILE. Some tasks involve reviewing or editing an existing query. One candidate describes time pressure, and another says the questions were ambiguous, so state your assumptions and validate edge cases.
Candidates describe discussing the case with interviewers, in one account live with the hiring manager. Expect follow-up questions on metric definition, experiment design, diagnostics and how you would approach real product problems. Interviewer style reportedly varies between candidates.
One candidate reports a round of two 1:1 interviews, then four more 1:1s with team members and cross-functional partners. Topics included career history, leadership, analytics and working with other functions. Prepare concrete examples and expect a few unexpected questions.
