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OpenAI's Applied organization is seeking a Data Engineering Manager to lead the Growth & Revenue data engineering team. This role owns the data strategy and execution for growth accounting, product partnerships, checkout, billing, payments, revenue, and monetization across all product surfaces. You will partner with Data Science, Business, and Engineering teams to connect product behavior to trustworthy subscriber, payment, and revenue measurement.
Key responsibilities include:
- Building, managing, and growing a high-performing, inclusive data engineering team
- Defining data strategy for owned data subject areas
- Delivering durable, well-modeled data products connecting product behavior, subscription state, checkout events, payment outcomes, and revenue
- Establishing trusted metric definitions and data quality standards for product, growth, finance, and executive decision-making
- Partnering with Data Science and Product teams to support experimentation, causal measurement, funnel analysis, and self-serve analytics
- Partnering with Finance and Financial Engineering to ensure analytical revenue views reconcile to financial truth and production billing systems
- Raising operational excellence for critical pipelines including reliability, observability, privacy, governance, and incident response
- Setting clear roadmaps, making principled tradeoffs, and communicating progress and risk across technical and business stakeholders
Success metrics include earning team and partner trust within 90 days, clarifying ownership boundaries, assessing the current data portfolio, and aligning on a prioritized roadmap. Within a year, Growth & Revenue stakeholders should rely on a smaller set of trustworthy, well-owned datasets and metrics for lifecycle, attribution, subscriber, billing, payment, monetization, and revenue decisions. The team should operate with clear goals, healthy execution rhythms, strong reliability standards, and a hiring and development plan matching the domain's ambition.
REQUIREMENTS:
- Deep experience leading and scaling data engineering teams in fast-moving product or technology environments
- Strong technical judgment across modern data systems: SQL, Python or Scala, Spark, orchestration, dimensional and event modeling, lakehouse or warehouse architectures
- Experience building trusted growth, lifecycle, attribution, subscription, billing, payments, revenue, or monetization data products at meaningful scale
- Ability to turn ambiguous business questions into durable data contracts, metric definitions, and technical roadmaps
- Strong partnership-building skills with Data Science, Product, Finance, Financial Engineering, GTM, and Engineering teams
- Deep commitment to data quality, privacy, security, and operational health of systems used for consequential decisions
- Excellent people leadership: hiring, talent development, clear feedback, and creating an inclusive environment where diverse perspectives thrive
