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OpenAI

Analytics Engineer, GTM

San Francisco$220k–$335kfulltimemidAdded today

About this role

Join OpenAI's Data team as an Analytics Engineer supporting the Go-to-Market organization. You'll build scalable data infrastructure, define key business metrics, and create self-service analytics tools that drive strategic decision-making across sales, support, and other GTM functions.

What you'll do

  • Partner with GTM teams to translate business needs into data models, metrics, and scalable technical solutions
  • Define, validate, and operationalize metrics that guide business planning and day-to-day decisions
  • Build and maintain scalable data pipelines and models integrating data from multiple sources
  • Create dashboards, reports, and self-service analytics products enabling independent stakeholder insights
  • Own the full lifecycle of metrics and data products from exploration through production maintenance
  • Communicate complex findings through presentations, memos, and visualizations tailored to audiences

What they're looking for

  • Advanced SQL and experience with large-scale datasets
  • ETL pipeline design and data modeling
  • Python or other quantitative programming languages
  • BI tools (Tableau, Looker) and self-service analytics design
  • Custom visualization frameworks (React, Streamlit, Plotly Dash)
  • Software engineering best practices and AI-assisted development tools
  • Business analysis and GTM domain knowledge
  • Data storytelling and executive communication
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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 ambiguous business problem you owned end-to-end—how did you frame it, and what was the impact?
  • Tell us about a time you had to choose between building a quick analysis versus a durable data product. What factors drove your decision?
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