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OpenAI

Data Engineer, Core Experimentation

Seattle (Remote)$293k–$325kfulltimemidAdded today

About this role

OpenAI's Statsig team is hiring a Data Engineer to design and operate data pipelines and core datasets powering experimentation, analytics, and safety systems across the company. You'll work at the intersection of data infrastructure, experimentation methodology, and product analytics, collaborating with researchers, engineers, and product teams to enable trustworthy, scalable decision-making.

What you'll do

  • Design, build, and manage data pipelines that ingest and integrate user event data into the data warehouse
  • Develop canonical datasets to track key product metrics including user growth, engagement, and revenue
  • Implement robust, fault-tolerant systems for data ingestion and processing at scale
  • Collaborate with Infrastructure, Data Science, Product, Marketing, Finance, and Research teams to understand and fulfill data needs
  • Participate in data architecture and engineering decisions
  • Ensure data security, integrity, and compliance with industry and company standards

What they're looking for

  • Python, Scala, or Java
  • Apache Spark (writing, debugging, optimization)
  • Distributed processing frameworks (Hadoop, Flink)
  • Distributed storage systems (HDFS, S3)
  • ETL schedulers (Airflow, Dagster, Prefect)
  • Data warehouse architecture and design
  • SQL and data modeling
  • Experience with large-scale data systems
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

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Likely interview questions

  • Walk us through a complex data pipeline you've designed—what were the key challenges and how did you ensure fault tolerance and scalability?
  • Describe your experience optimizing Spark jobs for performance. What bottlenecks have you encountered and how did you address them?
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