OpenAI Data Engineer interview typically runs 5 rounds: hiring manager screen, homework assignment, project presentation, technical rounds, and stakeholder rounds. The process spans roughly two months and is distinguished by a take-home project presentation central to evaluation.
$200K
Avg. Base Comp
$1170K
Avg. Total Comp
5-6
Typical Rounds
2-3 months
Process Length
From what we've seen in this process, OpenAI is less interested in whether you can write a perfect SQL query and more focused on how you think — about metrics, about systems, and about the people who consume data. The homework presentation is the clearest signal of this. It's not a portfolio show-and-tell; it's a structured interrogation of your decision-making. Candidates who treat it as a formality get caught flat-footed when interviewers probe the why behind every design choice.
The pipeline design question — specifically around monthly active users — is deceptively simple. MAU sounds like a standard metric, but the real test is whether you can surface the ambiguities: What counts as 'active'? How do you handle late-arriving data? What does the downstream consumer actually need? A recurring theme in this process is that OpenAI wants engineers who define the problem before they solve it. That instinct matters more here than raw technical fluency.
The stakeholder rounds are also worth taking seriously. At a company where the product is moving this fast, the ability to translate data work into decisions for non-technical partners isn't a soft skill — it's a core job requirement. We'd expect OpenAI to weight this heavily given how cross-functional the data function needs to be in a high-growth AI environment.
Synthetized from 1 candidates reports by our editorial team.
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| Question | |
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| Hurdles In Data Projects | |
| Resumable Fact Table Load | |
| Instagram TV Success | |
| Group Success | |
| Skewed Pricing | |
| Unlimited Plan Abuse | |
| Messenger Service Design | |
| Scalable Data Pipelines | |
| Cloud-Agnostic Deployments | |
| Facebook Story Success | |
| LRU Cache 1 | |
| Statistically Significant Test | |
| Programming Risk Combat | |
| Weighted Average With Missing Dates | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Experiment Validity | |
| Prime to N | |
| Largest Salary by Department | |
| String Shift | |
| Last Transaction | |
| Random SQL Sample | |
| Find the Missing Number | |
| Paired Products | |
| Monthly Customer Report |
Synthesized from candidate reports. Individual experiences may vary.
An introductory conversation with the hiring manager that serves as an initial screening. Expect a mix of background discussion and role-fit questions.
Candidates choose a past project they've worked on and prepare a presentation around it. The project should be something you know deeply, as it becomes the centerpiece of the next round.
A walkthrough of the project you selected for the take-home. Interviewers ask questions throughout and at the end, so be prepared to defend design decisions and discuss tradeoffs in depth.
Two rounds covering SQL fundamentals and pipeline design thinking. Expect standard SQL questions as well as conceptual system design questions such as how you would architect a data pipeline to calculate monthly active users.
Two conversational rounds focused on cross-functional collaboration and communication. Interviewers assess how you work with non-technical partners and translate data insights for broader audiences.