Analytics Engineer, GTM
OpenAI
- Pay
- $220k–$335k
- Workplace
- Hybrid
- Location
- San Francisco · California · United States
- First seen
- 10 days ago
- Last seen
- 4 hours ago
- Board
- Ashby
Summary
ABOUT THE ROLE As a member of the Data team within the Go-to-Market organization, you will help build a data-driven culture, improve decision-making, and advance strategic initiatives through analytics.
Posting
ABOUT THE ROLE As a member of the Data team within the Go-to-Market organization, you will help build a data-driven culture, improve decision-making, and advance strategic initiatives through analytics. This is a full-stack data role spanning data modeling, metric definition, visualization, analysis, and self-service tooling. You will build trusted, scalable data sources and products that give the business reliable, actionable insights. The work calls for judgment: you will choose the tool, approach, and level of investment that best fit each problem, from a focused analysis to a durable production data product. As a core partner to the GTM organization, you will address both foundational and ad hoc analytics needs. You will turn complex data into clear narratives that help technical and non-technical audiences understand what is happening, why it matters, and what they should do next. IN THIS ROLE, YOU WILL - Partner closely with GTM teams to proactively identify high-impact questions and translate business needs into data models, metrics, analyses, and scalable technical solutions. - Define, source, validate, and operationalize the metrics that guide the business, helping teams incorporate them into planning and day-to-day decisions. - Lead cross-functional data projects across established and emerging business areas, including setting the data strategy for greenfield domains. - Build scalable data models and pipelines that integrate and transform data from multiple sources into trusted, accessible datasets. - Create dashboards, reports, analytical tools, and other data products that enable stakeholders to answer questions independently. - Own the lifecycle of metrics, analytical models, and data products from initial exploration and prototyping through production and ongoing maintenance. - Choose the most effective approach for each problem—whether an analysis, metric, data model, visualization, or self-service product—based on the audience, urgency, complexity, and expected value. - Exercise strong judgment when prioritizing competing requests, balancing immediate business needs with investments that improve the long-term quality and scalability of the data ecosystem. - Use AI-assisted development tools to increase productivity while maintaining clear, tested, and maintainable code, data models, and documentation. - Turn complex findings into clear, persuasive narratives through presentations, written memos, dashboards, and oth