Doordash Data Engineer interviews typically start with a SQL and coding screen, then move to an onsite loop covering business-case, data-modeling, system-design, and behavioral rounds, based on candidate reports.
$213K
Avg. Base Comp
$307K
Avg. Total Comp
5-6 rounds
Typical Rounds
Not reported
Process Length
DoorDash's data engineer interviews lean heavily on SQL and data modeling rather than pure algorithm grinding. Candidates report an initial technical screen that mixes SQL with a coding question. One solved an anagram problem alongside a SQL question, while another faced four SQL questions and one Python problem in a single screen. A third was told to expect four SQL questions in 20 minutes but instead got two long, detailed queries in 30 minutes, along with a greedy-style coding problem. It's worth preparing for depth as well as coverage. Once through the screen, the onsite loop typically includes a business-partner or case round where you react to a metric change, for example investigating why a metric increased or dropped. It also includes a data-modeling round where you design a schema from a business situation, such as subscription churn, fitness-app metrics, or an ETL pipeline for user behavior, and then write SQL against your own design. One candidate also described a system-design round building an end-to-end batch and real-time pipeline, calling it the hardest part because there was no single right answer. Another instead got a live, debuggable coding round on a shared platform. A hiring-manager round that is mostly behavioral typically closes the loop. Interviewers seem to reward candidates who narrate their reasoning and ask clarifying questions early, since one candidate was tripped up by an ambiguous prompt. This guide reflects a handful of candidate reports, so treat the exact round count as directional rather than a fixed script.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Doordash process.
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Topics based on recent interview experiences.
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| Question | |
|---|---|
| Experiment Validity | |
| Month Over Month | |
| Monthly Customer Report | |
| Average Order Value | |
| Over-Budget Projects | |
| Longest Streak Users | |
| Daily Retention Summary | |
| Post Composer Drop | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Netflix Retention | |
| Marketing Channel Metrics | |
| WAU vs Open Rates | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Instagram TV Success | |
| Group Success | |
| How Many Friends | |
| Count Transactions | |
| Uber User Journey | |
| Biggest Tip | |
| Data Pipelines and Aggregation | |
| Recruiting Leads | |
| Sample Time Series | |
| A/B Testing a Checkout Button Change | |
| Success Measurement | |
| Job Training Program Evaluation | |
| Celebrity Mentions | |
| Unbiased Estimator |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported a quick recruiter response after applying online, followed by scheduling for a technical screen. Another candidate's recruiter described the upcoming SQL screen's format in advance, but the actual round differed from what was described, so treat recruiter previews as directional rather than exact.
The technical screen typically combines SQL with a coding question. Reports vary from a single LeetCode-style problem (for example, an anagram question) paired with one SQL question, to a screen with four SQL questions and one Python problem. One candidate got two long, detailed SQL questions in 30 minutes. Topics mentioned include nested-loop queries, LAG/LEAD window functions, and greedy-style coding prompts, and that candidate advised preparing for dynamic programming as well.
Two candidates describe a business-partner round focused on product and metric thinking rather than code. One reported prompt asked how you'd investigate what happened after a metric increased, while the other candidate's business-partner round leaned into product sense. This round appears to weigh communication and business judgment as much as technical correctness.
A data-modeling round shows up across multiple reports as one of the most distinctive parts of the loop. Candidates are given a business scenario, such as subscription cohort churn and conversion, fitness-app metrics, or an ETL pipeline for user behavior. They are asked to design the entity structure or fact tables, and in some cases to write SQL queries against the schema they just created. One candidate found the round was less about a perfect schema and more about walking through reasoning step by step.
One candidate described a system-design round asking for an end-to-end batch and real-time pipeline design, calling it the hardest round because there was no single right answer. Another candidate instead faced a medium-difficulty live coding round on a platform like HackerRank or CodePair that included a debugging component. That candidate had to trace through and fix code on the fly rather than just write it from scratch.
The loop typically closes with a hiring-manager round that candidates describe as mostly behavioral, in one case framed as scenario-based questions.
