OpenAI Data Engineer Interview Guide

OpenAI's data engineers aren't supporting AI from the sidelines. They're building the pipelines that directly feed model training, RLHF feedback loops, and the serving infrastructure behind products like ChatGPT and Codex. From hundreds of mock interviews we've run at DataInterview, the candidates who fail this loop almost always underestimate how deeply the role sits at the intersection of data engineering and ML infrastructure.

OpenAI Data Engineer Role

You're joining a lean data engineering team responsible for the pipelines that power model training, internal evaluation systems, and API metering for a product with massive scale. Concretely, that means building Dagster-orchestrated DAGs for training data ingestion, maintaining RLHF preference pipelines, and ensuring billing data reconciles API calls to invoices without drift. Success after year one looks like owning an end-to-end pipeline domain (training data preparation, eval infrastructure, or usage analytics) and having shipped at least one system that researchers or product teams depend on daily.

A Typical Week

What catches most candidates off guard is how little of the week is pure coding. Infrastructure work and meetings together consume nearly as much time, and days like Wednesday can be entirely eaten by debugging a null-rate spike in the RLHF pipeline and then hashing out data lineage requirements with the safety team. The Friday on-call handoff is real operational work, not a formality, because when a pipeline feeding model training breaks over the weekend, someone needs a runbook that actually works.

Projects & Impact Areas

Training data pipelines form the backbone: ingestion, deduplication (think MinHash over web-crawled corpora), filtering, and quality scoring for datasets that directly shape the next model release. Billing and usage analytics carry equally high stakes, since enterprise revenue depends on metering accuracy that reconciles every API call to an invoice. A newer and fast-growing frontier is agent infrastructure, where you're building the plumbing for Codex tool-call logging, agent execution traces, and the retrieval-augmented generation data stores that support search and retrieval features.

Skills & What's Expected

Overrated: SQL wizardry in isolation. You need strong SQL, sure, but production-grade Python, real testing discipline, and CI/CD fluency matter more here because the engineering bar is set at software engineer level, not traditional analytics engineer. Underrated: understanding how your pipelines serve ML workloads. You won't be training models, but you need to reason about why a 12% null rate in a preference ranking column breaks RLHF, or why dataset versioning with content hashing matters for training reproducibility. Cloud cost optimization on Azure is another sleeper skill, because when inference is this expensive, nobody tolerates a pipeline that wastes compute.

Levels & Career Growth

The widget shows the level bands, but here's what it doesn't tell you: L5 (Staff) almost always requires demonstrated ownership of org-wide data platform decisions, like defining the architectural vision for dataset versioning or training data governance across multiple research teams. The gap between L4 and L5 at OpenAI specifically comes down to whether you're shaping how the company's data infrastructure evolves as new model families and product lines spin up, not just executing within your team's backlog. OpenAI's rapid growth means new teams and scope emerge constantly, which creates real promotion opportunity, but also means the goalposts can shift as the org restructures around you.

Work Culture

OpenAI is intense, and they'll tell you that themselves. The company quietly updated its core values, swapping "thoughtful" for "intense" and leaning harder into commercial velocity, per Semafor's reporting. Shipping cadence is aggressive (weekly product releases are common), and data engineers feel that pressure through pipeline reliability SLAs.

You're expected in the SF Mission district office Tuesday through Thursday, with most data engineers clustering those days to overlap with the research teams they support. The upside is real proximity to the people building frontier models. The downside is a lean team relative to the data volume, which means on-call rotations have teeth.

OpenAI Data Engineer Compensation

OpenAI recently transitioned from Profit Participation Units (PPUs) to RSUs, with a vesting cliff reduced to six months instead of the older structure. The RSU grant size is where candidates have the most negotiation room, since base salary, sign-on bonuses, and equity can all be discussed, but from what candidates report, the grant is where OpenAI shows the most flexibility.

Your strongest move is to highlight unique skills in areas OpenAI actually struggles to hire for: experience with petabyte-scale training data pipelines, RLHF feedback infrastructure, or cost optimization on Azure compute. Specificity about how your background maps to OpenAI's data problems carries more weight than a generic counter-ask.

OpenAI Data Engineer Interview Process

Weak system design is one of the most common reasons candidates get rejected, and it's easy to see why. OpenAI's system design round doesn't reward generic data warehouse diagrams. You need to speak to AI-native constraints: how would you architect ingestion for massive conversation logs, or build a pipeline that feeds continuous model evaluation at scale? If your designs don't reflect the realities of LLM training and serving workloads, that round will hurt you.

The Product Sense & Metrics round is the one most candidates sleepwalk into. OpenAI includes it because they want data engineers who can reason about things like ChatGPT Enterprise retention drivers or success metrics for Codex agent workflows, not just build tables. Walk in with opinions about how you'd instrument and measure their actual products, because from what candidates report, treating that round as a formality is a fast path to a "no."

OpenAI Data Engineer Interview Questions

WITH ranked AS (
  SELECT
    user_id,
    event_ts,
    request_id,
    tokens_in,
    tokens_out,
    ROW_NUMBER() OVER (
      PARTITION BY request_id
      ORDER BY event_ts ASC
    ) AS rn
  FROM usage_events
  WHERE event_ts >= (CURRENT_DATE - INTERVAL '30 days')
), deduped AS (
  SELECT
    user_id,
    event_ts,
    request_id,
    tokens_in,
    tokens_out
  FROM ranked
  WHERE rn = 1
)
SELECT
  CAST(event_ts AS DATE) AS event_date,
  COUNT(DISTINCT user_id) AS dau,
  SUM(COALESCE(tokens_in, 0) + COALESCE(tokens_out, 0)) AS total_tokens
FROM deduped
GROUP BY 1
ORDER BY 1;

from __future__ import annotations

import heapq
import json
from collections import defaultdict
from typing import DefaultDict, Iterable, List, Tuple


def top_k_users_by_tokens(
    lines: Iterable[str],
    start_ms: int,
    end_ms: int,
    k: int,
) -> List[Tuple[str, int]]:
    """Return top-k (user_id, total_tokens) within [start_ms, end_ms).

    Input lines are newline-delimited JSON strings with keys:
      - ts_ms: int
      - user_id: str
      - tokens: int

    Ties are broken by lexicographically smaller user_id.
    """
    if k <= 0:
        return []
    if end_ms <= start_ms:
        return []

    totals: DefaultDict[str, int] = defaultdict(int)

    for line in lines:
        line = line.strip()
        if not line:
            continue
        obj = json.loads(line)
        ts = int(obj["ts_ms"])
        if start_ms <= ts < end_ms:
            user_id = str(obj["user_id"])
            tokens = int(obj["tokens"])
            totals[user_id] += tokens

    # Sort by (-tokens, user_id) and take first k.
    # For large cardinality you could use nlargest with a key, but sorting is fine for small data.
    ranked = sorted(totals.items(), key=lambda kv: (-kv[1], kv[0]))
    return ranked[:k]


# Optional heap-based variant for very large number of users.

def top_k_users_by_tokens_heap(
    lines: Iterable[str],
    start_ms: int,
    end_ms: int,
    k: int,
) -> List[Tuple[str, int]]:
    if k <= 0 or end_ms <= start_ms:
        return []

    totals: DefaultDict[str, int] = defaultdict(int)
    for line in lines:
        line = line.strip()
        if not line:
            continue
        obj = json.loads(line)
        ts = int(obj["ts_ms"])
        if start_ms <= ts < end_ms:
            totals[str(obj["user_id"])] += int(obj["tokens"])

    # Build a heap of size k storing the current worst element.
    # We want to keep the best by (tokens desc, user_id asc).
    # So the heap should order by (tokens asc, user_id desc) as "worst first".
    heap: List[Tuple[int, str]] = []  # (tokens, negated order user)

    for user_id, total in totals.items():
        entry = (total, user_id)
        if len(heap) < k:
            heapq.heappush(heap, entry)
            continue

        # Compare against smallest total; if tie, prefer lexicographically smaller user_id,
        # which means lexicographically larger user_id is worse.
        worst_total, worst_user = heap[0]
        if total > worst_total or (total == worst_total and user_id < worst_user):
            heapq.heapreplace(heap, entry)

    # Heap is not ordered; sort for final deterministic output.
    return sorted(heap, key=lambda kv: (-kv[0], kv[1]))

The distribution skews heavily toward questions where you can't fake domain knowledge. Designing an RLHF feedback pipeline or debugging hallucinated citations in a ChatGPT agent's retrieval layer requires you to understand how OpenAI's products actually consume data, not just how to wire up Airflow DAGs. The biggest prep mistake is treating this like a standard data engineering loop and spending most of your time on query syntax and algorithm drills while ignoring the AI infrastructure problems that dominate the interview and, frankly, the job.

Practice realistic questions across all six areas at datainterview.com/questions.

How to Prepare for OpenAI Data Engineer Interviews

OpenAI's product surface is expanding fast, from Codex as a cloud-based coding agent to Atlas for real-time information synthesis to a planned hardware device in 2026. What the widget can't show you is the implication: each new product surface likely means new telemetry schemas, new ingestion patterns, and new data quality contracts that a data engineer would own. Your prep should focus on understanding how these products create distinct data problems, not just what they do for end users.

Most candidates blow the "why OpenAI" question by talking about AGI in the abstract. Interviewers at OpenAI have heard that pitch from everyone who also applied to their competitors. What separates you is naming a specific data problem behind a specific product: "I want to build the reconciliation pipeline behind your API billing, because your enterprise AI report makes clear how much of your revenue depends on metering accuracy" shows you've done homework that a generic mission statement never will.

Try a Real Interview Question

| event_id | event_ts            | model      | status | latency_ms |
|----------|---------------------|------------|--------|------------|
| e1       | 2026-02-20 01:10:00 | gpt-4o     | ok     | 120        |
| e2       | 2026-02-20 02:20:00 | gpt-4o     | error  | 800        |
| e3       | 2026-02-20 03:30:00 | gpt-4o-mini| ok     | 60         |
| e4       | 2026-02-21 09:00:00 | gpt-4o     | ok     | 110        |
| e5       | 2026-02-21 10:00:00 | gpt-4o     | ok     | 130        |
| e6       | 2026-02-21 11:00:00 | gpt-4o-mini| error  | 90         |

WITH daily AS (
  SELECT
    CAST(event_ts AS DATE) AS event_day,
    model,
    COUNT(*) AS total_requests,
    SUM(CASE WHEN status = 'ok' THEN 1 ELSE 0 END) AS successes
  FROM prompt_events
  GROUP BY 1, 2
)
SELECT
  event_day,
  model,
  total_requests,
  successes,
  CAST(successes AS DECIMAL(18,6)) / NULLIF(total_requests, 0) AS success_rate
FROM daily
WHERE total_requests >= 2
ORDER BY event_day ASC, model ASC;

OpenAI hires data engineers under the software engineer title, which means their coding rounds reward production-quality Python over textbook algorithm tricks. Problems that involve parsing semi-structured data, enforcing validation rules, or transforming nested records map closely to the daily work of feeding messy real-world inputs into training and analytics pipelines for products like ChatGPT and Codex. Practice this kind of problem at datainterview.com/coding.

Test Your Readiness

Identify your weak spots across OpenAI's specific topic mix before you spend hours drilling the wrong area. Realistic practice questions are at datainterview.com/questions.