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Home / Compensation / OpenAI / Data Engineer
Total compensation for a OpenAI Data Engineer ranges from about $290K at the entry level to $2.5M+ at the most senior bands, with a typical mid-level package around $680K. Below is the full breakdown by level - base salary, annual equity, and bonus - from public-source data.
Public-source estimates (Beta). Last updated 2026-05. Figures are annualized USD.
| Level | Years | Base (p50) | Equity/yr (p50) | Bonus (p50) | Total (p25-p90) |
|---|---|---|---|---|---|
| L3Data Engineer (Junior) | 1-3 YOE | $245K | $80K | - | $290K - $490K |
| L4Data Engineer | 3-7 YOE | $285K | $185K | - | $400K - $770K |
| L5Senior Data Engineer | 6-11 YOE | $330K | $320K | - | $540K - $1.1M |
| L6Staff Data Engineer | 10-15 YOE | $380K | $545K | - | $760K - $1.6M |
| L7Senior Staff Data Engineer | 13+ YOE | $450K | $870K | - | $1.0M - $2.5M |
Junior DE. Training-pipeline focus.
Mid-level. Common entry from FAANG data orgs.
Senior IC. Evals-infra premium.
Staff IC.
Senior staff. Limited external hires.
OpenAI data engineering tracks the SWE ladder closely. Training-data pipelines, evals infrastructure, and applied-research data tooling are major hiring areas. PPU equity applies; bands track SWE within ~5%.
6 rounds. 5 of them can be practised here - each one below links the rep that trains it.
Recruiter screen
30 minutes. Background, level calibration, role fit. OpenAI recruits across infrastructure, product, research engineering, and applied AI - the recruiter will clarify which track you're on.
Technical phone screen
60 minutes. One to two coding problems. Algorithmic, Medium-to-Hard difficulty. They evaluate speed, clarity, and how you communicate tradeoffs while coding.
Free rep: Coding challenges with a real sandbox →Onsite: Coding round 1
60 minutes. Hard algorithmic problems or Medium with deep follow-ups. Trees, graphs, dynamic programming, and system-level coding (LRU cache, rate limiter, scheduler) all appear.
Free rep: Coding challenges with a real sandbox →Onsite: Coding round 2
60 minutes. More applied or systems-flavored coding. May involve designing a class hierarchy, building a small data pipeline, or extending a working system.
Free rep: Coding challenges with a real sandbox →Onsite: System design
60 minutes. ML-infrastructure flavored. Design a model serving system, an evaluation harness, a prompt caching layer, or a distributed training job scheduler. Interviewers care about failure handling, scale assumptions, and whether you can reason about novel constraints.
Free rep: Design walkthrough: Design a Rate Limiter (API Throttling) →Onsite: Behavioral / values
45-60 minutes. Mission alignment, handling ambiguity, and working at the frontier. OpenAI is explicit about hiring people who care about the AGI mission. Generic 'AI is exciting' answers don't land - they probe for specific reasoning about where AI is going and what responsible development means to you.
Free rep: STAR answers: Dealing with Ambiguity →The full OpenAI prep pack → what they value, topic emphasis, and the questions candidates ask.
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