What to expect in an OpenAI Data Engineer interview: a hiring-manager conversation, a remote Python stream-processing screen, and a reported onsite covering projects, cross-functional work and data infrastructure.
$290K
Avg. Base Comp
$550K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the OpenAI Data Engineer interview in two different shapes, so prepare for both. One candidate reports two rounds: a hiring-manager conversation, then a remote technical screen. The hiring manager looked at product fit and asked about scope and familiarity with outside tools. The technical screen was a simple Python stream-processing class design: maintain the sum of a fixed-size window over a stream of integers, support a get method, and handle what happens once the window is full. That candidate was rejected. Clean, simple code and clear handling of edge cases like a full window are worth rehearsing.
Another candidate prepared for an onsite of four conversations: a project-experience discussion, a cross-functional interview, a hiring-manager conversation, and a dedicated data-infrastructure round. The cross-functional round was described as comparatively straightforward if you can talk to a PM, explain tradeoffs, and align on goals. Be ready to walk through a data-infrastructure project in depth and to frame your design decisions in terms of the product they serve.
An Analytics Engineer candidate, interviewing for a different role, reported a homework presentation with questions throughout, SQL, and a conceptual pipeline design question on calculating monthly active users. That account is only adjacent, but it suggests that thinking through metric definitions and pipeline structure is worthwhile preparation.
Synthesized from 4 candidate reports by our editorial team.
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Topics based on recent interview experiences.
| Question | |
|---|---|
| Google Maps Improvement | |
| Skewed Pricing | |
| Resumable Fact Table Load | |
| Hurdles In Data Projects | |
| Instagram TV Success | |
| Group Success | |
| Transformer Encoder Layer | |
| Data Pipelines and Aggregation | |
| Moving Window | |
| Messenger Service Design | |
| Unlimited Plan Abuse | |
| Swipe Payment API | |
| Scalable Data Pipelines | |
| Cloud-Agnostic Deployments | |
| Facebook Story Success | |
| LRU Cache 1 | |
| Programming Risk Combat | |
| Statistically Significant Test | |
| Weighted Average With Missing Dates | |
| Trial Test Analysis | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Employee Salaries | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Last Transaction | |
| Largest Salary by Department | |
| Top 3 Users |
Synthesized from candidate reports. Individual experiences may vary.
Both Data Engineer accounts include a hiring-manager conversation. One candidate says it covered product fit, the scope of past work, and familiarity with outside tools. Prepare concise project stories you can defend under follow-up questions, and be ready to explain how you would work within the team.
One candidate reports a remote technical screen built around a simple Python class design. The task was to maintain the sum of a fixed-size window over a stream of integers, expose a get method, and define behavior when the window fills. Expect to reason about state and edge cases, not only write code.
Another candidate prepared for an onsite that included a project-experience discussion and a cross-functional interview. The cross-functional round was described as manageable if you can communicate with a PM, explain tradeoffs, and align on goals. Practice walking through prior work in depth.
The same onsite account includes a dedicated data-infrastructure conversation alongside a hiring-manager discussion. The candidate focused technical preparation here and framed infrastructure decisions around product partnership. Specific questions were not reported, so review pipeline design and metric computation broadly.
