Just finished the data engineer loop at OpenAI last month. Sharing notes while they're fresh because I couldn't find anything specific when I was prepping.
The process was: recruiter screen, then a 45-min technical phone screen, then a 4-hour onsite (3 technical rounds plus one values/culture round). The whole thing took about 5 weeks from first contact to verbal offer.
Phone screen: Focused on SQL and basic data modeling. They gave me a schema, described a business question, and asked me to write a query. The question itself wasn't insane hard, but they pushed on edge cases: what happens with nulls, how would you handle timezone differences, could this query handle 10x the data volume. If you haven't thought about query performance in a while, brush up before this call.
Onsite round 1 (data pipelines): This was the meaty one. They described a realistic data ingestion scenario, model training data at scale, and asked me to walk through how I'd build the pipeline. Things they probed: fault tolerance, idempotency, exactly-once semantics, monitoring. Airflow came up. So did whether I'd use streaming vs batch for different parts. Very practical, no trivia.
Onsite round 2 (coding + data structures): Standard-ish coding. Two LeetCode-style problems, medium difficulty. One was clearly graph-adjacent. They're not obsessing about hard problems but they want clean code and good communication. Explain your approach before you start writing.
Onsite round 3 (system design, data flavor): Design a feature store or something in that space. I got a prompt roughly like: how would you build a system to serve real-time features for model inference? Not just the happy path, they want you to discuss consistency, latency tradeoffs, schema evolution.
Values round: Behavioral and very mission-focused. Why AI safety matters to you. What responsible data practices look like in an AI context. This one actually matters at OpenAI; it's not just a formality.
Comp note: my offer for a mid-senior DE was in the range I'd expect for top-tier SF, roughly competitive with Meta/Google but with more equity upside given the company stage. Don't take the first number.
Happy to answer specifics in replies.