OpenAI Data Engineer Interview: Process and Preparation

You have a recruiter email from OpenAI and want to know what comes next before you agree to a schedule. This section sets out what OpenAI itself states about its process, then the stages a data engineering loop usually contains. Treat the published facts as fixed points and everything else as preparation.

OpenAI publishes a general interview guide rather than a breakdown for each role, and it does not publish a separate loop for data engineers. What it does state applies across its hiring:

• Résumé review. The recruiting team typically takes one week to review a résumé and reply.
• Decisions after each stage. Candidates hear within a week after each stage whether they have advanced.
• Final interviews. Candidates typically spend 4-6 hours in final interviews with 4-6 people over 1-2 days.
• Format. Interviews are virtual by default, and candidates may choose to interview onsite at the San Francisco office.
• Final decision. Candidates should expect to hear back within one week of their final interviews.

OpenAI does not publish how many interviews come before the finals for a data engineer, what each is called, or the order they run in. Your recruiter is the reliable source for that, so ask which areas each conversation covers and whether any part allows AI tools.

For planning, the stages below are general preparation categories for data engineering roles at large technology companies, not OpenAI's round names:

• Recruiter conversation. Background, motivation, the team, and logistics such as virtual or onsite finals.
• Technical screen. Usually SQL and some coding, often with questions about a pipeline you have run in production.
• Hiring manager conversation. The systems you have built, the scale and messiness of the data you handled, and the trade-offs you made.
• Final interviews. Deeper coding, data modeling, pipeline or system design, and behavioral conversations, spread across the 4-6 people OpenAI describes.

Posts on Reddit, Glassdoor and similar forums describe individual loops in detail. Each reflects one candidate on one team at one point in time, so use them to see the range of topics rather than as a schedule.

For engineering interviews, OpenAI says it generally looks for well-designed solutions, high-quality code, optimal performance and good test coverage. Those criteria are written for engineers broadly, but they map closely onto data work. The sample questions on this page are practice prompts, not questions OpenAI has confirmed it asks.

• Well-designed solutions: modeling and pipeline design. Choosing the grain of a table, separating facts from dimensions, deciding between batch and streaming, and planning for backfills, late data and schema changes. A design that survives changed requirements reads as well designed; one that only fits the original prompt does not.
• High-quality code: Python and SQL you would ship. Clear names, small functions, explicit handling of nulls and malformed records, and joins you can justify. Interviewers usually care whether you can explain why a query is correct, not only whether it runs.
• Optimal performance: cost and scale. Partitioning, avoiding full scans, reducing shuffles in distributed jobs, and knowing when an approximate answer is acceptable. Say where the bottleneck is before proposing a fix.
• Good test coverage: data quality as testing. For a data engineer this means unit tests for transforms and checks on the data itself: row counts against expectations, unique keys, freshness, and alerts when a source changes shape.
• Behavioral judgment. Ownership of a pipeline failure, disagreements about metric definitions, and how you decided what not to build.

OpenAI also states that expectations for AI and other tools vary by interview. Some formats intentionally allow them, and others are designed to assess independent problem-solving without AI tools. In an assisted format, the skill on display is how you direct, check and correct the tool's output; in an unassisted one, you produce the SQL and code yourself. Follow the rules each interviewer sets, and ask when they are unclear. More practice prompts grouped by skill are in the question banks.

OpenAI does not publish role-by-role differences, so the comparison below is general practice for data engineering, not a statement about OpenAI's other loops.

• Compared with software engineering. Less weight on algorithm puzzles and more on SQL, data modeling and pipeline reliability. Design questions tend to center on how data moves, lands and gets corrected rather than on request-serving services. The engineering criteria OpenAI names still apply, so code quality and tests matter as much as in any engineering loop.
• Compared with data science. Less statistics and experiment design, more on building the tables that analysts and researchers depend on. You may still be asked how a metric is defined, because a data engineer often owns the table behind it.
• Compared with analytics engineering. The line is blurry. Analytics roles usually lean toward modeling and SQL for reporting, while data engineer roles go deeper into ingestion, distributed processing and infrastructure.

Compared with other large companies, OpenAI's published process has features worth planning around. The finals are concentrated into 1-2 days, so stamina matters: your last conversation of the day gets the same scrutiny as your opening one. The tool policy changes by interview, so ask about it before each session rather than assuming. And the commitment to reply within a week after each stage lets you plan other interviews and offers with more confidence. Guides to how other companies structure their loops sit under company interviews.

OpenAI publishes some timing detail, though not a full timeline. The recruiting team typically replies to a résumé within one week, candidates hear within a week after each stage, and the final decision usually comes within one week of the final interviews. The finals themselves typically take 4-6 hours with 4-6 people over 1-2 days.

What OpenAI does not publish is how many stages a data engineer goes through before the finals, so the total length of a process cannot be worked out from the guide alone. Ask your recruiter how many conversations to expect and plan your notice period and other interviews around that answer.

Difficulty is easier to describe in qualitative terms. Each stage acts as a gate, since you hear after each one whether you have advanced, so a weak screen ends the process before any design conversation starts. The harder part for most candidates is depth under follow-up: you sketch a pipeline, then the interviewer adds late-arriving events, a schema change in an upstream source, and a question about how you would know the output is wrong. Candidates who have operated pipelines in production usually handle that drift better than those who have only built them.

Senior candidates should expect more weight on judgment: what not to build, how to explain a trade-off to a researcher or product partner, and how they handled a mistake they owned.

Build preparation around the criteria OpenAI names for engineering interviews, then add the tool question and the behavioral side.

• Well-designed solutions. Pick a domain you know, such as event logs or usage metering, and sketch the tables, their grain and the pipeline that fills them. Then stress the design: a rerun after partial failure, a backfill, late data, a renamed column.
• High-quality code. Write Python transforms against messy, nested records and keep them readable. Practice window functions, deduplication and sessionization in SQL until you can write them without a reference, and state the grain of each result before you run it.
• Optimal performance. For each design, name the expensive step and how you would make it cheaper, such as partition pruning, incremental processing or pre-aggregation.
• Good test coverage. Add tests to every practice solution and describe the data quality checks you would run in production. Adding tests unprompted shows the habit the criterion describes.
• With and without AI tools. Practice some problems with an assistant and some without, so neither format surprises you. In the assisted format, narrate how you verify what the tool produced.
• Stamina for the finals. Rehearse back-to-back mock interviews in a single sitting, since the finals concentrate 4-6 hours of conversations into 1-2 days.
• Behavioral stories. Prepare a pipeline failure you owned, a disagreement about a metric definition, and a trade-off you would make differently now.

Consider a data engineer whose experience is mostly nightly batch jobs at a payments company. They spend early sessions on SQL window functions, then practice explaining how they would make a backfill safe to rerun, because that is where their real stories are. They also solve some problems with an AI assistant and some without, so the tool policy in a given interview does not change how they reason.

The last step is saying all of this aloud, because every stage is a conversation. SubcueAI's mock interview page lets you practice data engineering questions out loud before the real screen.