About the Company
The OpenAI Deployment Company (“DeployCo”) is the deployment arm of OpenAI, purpose-built to deliver enterprise AI transformation at scale. As model capabilities accelerate, deployment has become the primary constraint to realizing AI value creation within large enterprises. DeployCo bridges that gap. Our ambition is to build the company that defines AI deployment. We operate at the intersection of cutting-edge AI, enterprise transformation, and scaled services delivery. We partner with enterprise customers to identify where AI can transform their businesses and build solutions leveraging OpenAI’s models and products. We work with many of the world's largest and most influential companies to solve their most complex business challenges using the latest advances in AI. People who thrive at DeployCo get excited about ambitious and complex work. We are deep believers in the future with AI. We execute quickly, value intellectual honesty, and obsess over the solutions we build for our customers. DeployCo is being built from the ground up as a subsidiary of OpenAI and funded with a partnership of outside investors.
About the Role
We are seeking a Finance Data Analytics Senior Manager to build and own the data foundation for DeployCo’s AI-native FP&A function. Reporting to the FP&A lead, you will combine hands-on data skills with finance expertise to build reliable pipelines, maintain trusted datasets, analyze performance, and deliver clear recommendations. You will own the full finance data lifecycle, from connecting source systems and defining financial metrics to explaining business results. Working with FP&A, Accounting, and cross-functional teams, you will support budgeting, rolling forecasts, revenue and pipeline analysis, headcount and capacity planning, and management reporting. This is a hands-on role for someone who is equally comfortable writing SQL, troubleshooting a data pipeline, and explaining a financial analysis. You will build and maintain the underlying systems and use the data yourself to answer business questions, identify risks and opportunities, and recommend actions as the finance function grows.
This role is based in New York City.
In this role, you will:
Build and maintain finance data pipelines and models connecting ERP, planning, CRM, HRIS, and project delivery systems, with consistent financial metric definitions.
Own data quality and reliability through monitoring, troubleshooting, source reconciliations, automated checks, documentation, and access controls.
Analyze revenue, margins, pipeline conversion, and workforce capacity; explain performance drivers and variances, and maintain models for budgeting, forecasting, and scenario analysis.
Deliver clear management reporting and enable self-service analysis through trusted datasets, dashboards, and documented metrics that help finance and business teams answer their own questions.
Use Codex and ChatGPT to analyze finance data, develop recommendations, and automate recurring workflows, validating code, calculations, and conclusions and keeping outputs traceable.
Partner with FP&A, Accounting, IT, Engineering, and business teams to prioritize finance data needs, use approved platforms, and support tool adoption.
You may thrive in this role if you:
Bring approximately 6–10 years of relevant experience across finance, FP&A, accounting, analytics, data engineering, or finance systems, with meaningful hands-on experience in both finance and data.
Have personally built and maintained data pipelines and financial datasets used for recurring analysis and reporting, including ongoing troubleshooting and support.
Bring strong SQL and practical proficiency in Python or a comparable scripting language, with experience integrating APIs, querying databases, and automating data transformations.
Have a strong understanding of financial statements and hands-on experience in budgeting, forecasting, and revenue, cost, margin, and variance analysis; can explain the business drivers behind the numbers.
Have practical experience with data modeling, ETL/ELT, cloud data warehouses, and scheduled workflows; can turn inconsistent source data into reliable, well-documented finance datasets.
Are strong in Excel or Google Sheets and have built dashboards and reporting in a BI tool such as Power BI, Tableau, or Looker, connecting detailed data to useful financial analysis.
Apply sound data quality and control practices, including source reconciliations, automated checks, version control, documentation, and appropriate access to sensitive finance data.
Communicate clearly with finance, technical, and business stakeholders; translate ambiguous questions into analyses and recommendations, and explain assumptions and limitations.
Take ownership from initial build through ongoing maintenance, analysis, and reporting, prioritizing business needs and improving processes as the finance function grows.