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OpenAI

Analytics Engineer, GTM

San Francisco (Remote)$220k–$335kfulltimemidAdded today

About this role

Build scalable data infrastructure and analytics solutions for OpenAI's Go-to-Market organization. Design data models, define key metrics, create dashboards, and partner with GTM teams to translate business questions into actionable insights and self-service analytics products.

What you'll do

  • Partner with GTM teams to identify high-impact questions and translate business needs into data models, metrics, and technical solutions
  • Define, validate, and operationalize key business metrics that guide planning and decision-making
  • Build scalable data pipelines and models integrating multiple data sources into trusted, accessible datasets
  • Create dashboards, reports, and self-service analytics tools enabling stakeholders to answer questions independently
  • Own the full lifecycle of metrics and data products from exploration through production and maintenance
  • Translate complex findings into clear narratives through presentations, memos, and visualizations for diverse audiences

What they're looking for

  • Advanced SQL and experience with large-scale datasets
  • Data modeling and ETL pipeline design
  • Python or other quantitative programming languages
  • BI tools (Tableau, Looker, or similar)
  • Custom visualization frameworks (React, Streamlit, Plotly Dash)
  • Analytics storytelling and data communication
  • AI-assisted development tools
  • Judgment on prioritization and investment in data solutions
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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.

View all jobs at OpenAI

Likely interview questions

  • Walk us through a complex analytical project you led from ambiguous business question to recommendation—how did you prioritize and what was the impact?
  • Describe your experience building and maintaining metrics in production. How do you ensure data quality and stakeholder adoption?
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