Director of Data Engineering and Agentic AI Automation, Finance

About the TeamWe are looking for a Director of Data Engineering and Agentic AI Automation to lead the next generation of our finance data infrastructure. As OpenAI expands its Finance operations, we need scalable and trustworthy data systems to match the pace and complexity of our growth. This includes well-modeled, auditable data for revenue recognition, financial reporting, and planning, supported by reliable pipelines that connect ERP, planning, and operational systems. You will lead a group of analytics engineers, data engineers, and AI engineers to build the data pipelines that connect our internal engineering systems with enterprise platforms such as Oracle Fusion ERP. This role will also define the roadmap for agentic AI automation, enabling intelligent workflows, process automation, and AI-driven decision-making across Finance.This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.In this role, you will:Build and maintain scalable, auditable data infrastructure that powers accurate financial information, with a focus on revenue recognition, compute attribution, and close automation.Lead and grow teams of analytics engineers, data engineers, and AI engineers to deliver high-impact, intelligent data systems.Guide work across financial close and allocations automation, B2C revenue automation from engineering systems to ERP (including reconciliation with cash and source systems), and other mission-critical financial processes.Design and implement data pipelines connecting ERP, planning, and operational systems, including Oracle Fusion, Anaplan, and Workday.Build and support scalable, audit-proof architecture that enables reliable financial reporting and compliance.Develop data and AI-powered workflows that enhance forecasting accuracy, compliance automation, and operational efficiency.Create and maintain data marts and products that support stakeholders across Revenue, FP&A, Tax, Procurement, Hardware Accounting, and Controller teams.Define and enforce best practices for data modeling, lineage, observability, and reconciliation across finance data domains.Set the technical direction and manage team structure, mentoring engineers and overseeing contractors or system integrators to ensure delivery of high-quality outcomes.Partner with senior leaders across Finance, Engineering, and Infrastructure to align on priorities and integrate new automation capabilities.Ensure data systems are AI-ready and capable of supporting predictive analytics, autonomous agent workflows, and large-scale automation.Own and maintain Tier-1 data pipelines with strict SLA, data quality, and compliance standards.Drive the long-term roadmap for agentic AI enablement to build the foundation for “Finance on OpenAI.”You might thrive in this role if you have:12+ years in data engineering, with proven experience building and managing enterprise-scale, auditable ETL pipelines and complex datasetsProficiency in SQL and Python, with demonstrated experience in schema design, data modeling, and orchestration frameworksExpertise in distributed data processing technologies such as Apache Spark, Kafka, and cloud-native storage (e.g., S3, ADLS)Deep knowledge of enterprise data architecture, especially within Finance and Supply ChainFamiliarity with financial processes (close, allocations, revenue recognition) and supply chain data models (Supply and demand planning, procurement, vendor master), along with experience in ingesting data from internal engineering systems with large volumes of B2CExperience integrating with contract manufacturers and external logistics providers is a strong plusStrong track record of partnering with senior business stakeholders and translating complex requirements into scalable technical solutionsAbout OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humani ... (truncated, view full listing at source)