Analytics Engineer, GTM

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 other formats suited to the audience. You Might Thrive in This Role If You - Have 10+ years of experience in a relevant data role within fast-moving, results-oriented organizations. - Can independently structure and own ambiguous, high-impact business problems from initial framing through recommendation and execution. - Are highly autonomous, resourceful, and creative when navigating technical, operational, and stakeholder constraints. - Exercise strong judgment when deciding what to prioritize, how deeply to invest, and when a quick answer should become a durable data product. - Have deep SQL expertise and extensive experience working with large datasets and designing ETL workflows. - Understand effective software and analytics engineering practices and can use AI tools to move faster without creating brittle systems, unclear code, or unnecessary complexity. - Are proficient in a quantitative programming language, preferably Python. - Have experience with BI tools such as Tableau or Looker and know how to enable effective self-service analytics. - Have worked with custom visualization frameworks such as React, Streamlit, or Plotly Dash. - Can turn complex analysis into persuasive stories using memos, presentations, dashboards, and other formats. - Bring exceptional attention to detail and a strong commitment to accuracy. - Have delivered significant business impact, ideally within Sales, Finance, Support, or another GTM function. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records wil

10+ years of experience in a relevant data role within fast-moving, results-oriented organizations. Highly autonomous and capable of structuring and owning ambiguous, high-impact business problems. Deep SQL expertise and extensive experience with large datasets and ETL workflows. Proficiency in Python. Experience with BI tools such as Tableau or Looker and ability to enable effective self-service analytics. Experience with custom visualization frameworks such as React, Streamlit, or Plotly Dash.

Partner with Go-to-Market teams to translate business needs into data models, metrics, analyses, and scalable technical solutions. Define, source, validate, and operationalize metrics and incorporate them into planning and day-to-day decisions. Lead cross-functional data projects, including setting data strategy for new domains. Build scalable data models and pipelines transforming data from multiple sources into trusted datasets. Create dashboards, reports, analytical tools, and data products that enable stakeholders to answer questions independently. Own the lifecycle of metrics, models, and data products from exploration to production and maintenance. Choose effective approaches for problems (analysis, metric, data model, visualization, or self-service product) based on audience and value. Prioritize competing requests to balance immediate needs with long-term data quality. Use AI-assisted development tools to boost productivity while maintaining clean code, data models, and documentation. Turn complex findings into clear narratives via memos, dashboards, and presentations.