Data Engineering Manager, Growth & Revenue

OpenAI•San Francisco, CA

1d•$459,000 - $536,000•Onsite

About The Position

The Applied organization brings OpenAI’s most advanced technology to the world through products like ChatGPT and the APIs that power a growing ecosystem of developer and enterprise applications. Data Engineering builds and operates the trustworthy, secure, and reliable data systems that power decisions across OpenAI. We’re looking for a Data Engineering Manager to lead the Growth & Revenue data engineering team. This leader will own the data strategy and execution for the data subject areas spanning growth accounting across all product surfaces, product partnerships, checkout, billing, payments, revenue, and monetization, helping OpenAI understand how people adopt, engage with, and pay for our products. You will partner closely with several Data Science, Business, and Engineering partners to connect product behavior to trustworthy subscriber, payment, and revenue measurement.

Requirements

Deep experience leading and scaling data engineering teams in a fast-moving product or technology environment.
Strong technical judgment across modern data systems, including SQL, Python or Scala, Spark, orchestration, dimensional and event modeling, and lakehouse or warehouse architectures.
Built trusted growth, lifecycle, attribution, subscription, billing, payments, revenue, or monetization data products at meaningful scale.
Can turn ambiguous business questions into durable data contracts, metric definitions, and technical roadmaps.
Build unusually strong partnerships with Data Science, Product, Finance, Financial Engineering, GTM, and Engineering.
Care deeply about data quality, privacy, security, and the operational health of systems used for consequential decisions.
Excellent people leader: you hire well, develop talent, give clear feedback, and create an environment where diverse perspectives do their best work.

Responsibilities

Build, manage, and grow a high-performing, inclusive team across the Growth & Revenue data subject areas.
Define the data strategy for all the data subject areas you own.
Deliver durable, well-modeled data products that connect product behavior, subscription state, checkout events, payment outcomes, and revenue.
Establish trusted metric definitions and data quality standards so product, growth, finance, and executive leaders can make fast, consistent decisions.
Partner with Data Science and Product teams to support experimentation, causal measurement, funnel analysis, and scalable self-serve analytics.
Partner with Finance and Financial Engineering to ensure analytical revenue views reconcile to financial truth and production billing systems.
Raise operational excellence for critical pipelines, including reliability, observability, privacy, governance, and incident response.
Set a clear roadmap, make principled tradeoffs, and communicate progress and risk across technical and business stakeholders.