Product Finance Director, R&D Capital Allocation & Engineering Operations

Databricks•Mountain View, CA

25d•$201,400 - $307,650

About The Position

Databricks is seeking a Product Finance leader to own R&D capital allocation and engineering operations as the company scales. The role partners with senior Engineering and Product Operations leaders and covers product-level R&D return on investment, global workforce and location strategy, and the economics of AI-enabled software development. The Director will establish frameworks for measuring product-level ROI and operationalize those metrics to guide product investment and resource allocation. In partnership with Engineering, the role will model the relationship between AI developer tooling, internal token spending, headcount, and compute capital; measure the end-to-end cost of AI-enabled engineering, including impacts on cloud spend; and help optimize investments. The role will lead multi-year headcount planning across global engineering hubs, balancing cost per hire, talent density, and university recruiting pipelines. It will also coordinate R&D hiring plans with Workplace and Facilities leadership, global real estate strategy, and multi-year capital expenditure planning. The Director will scale R&D finance operations by building automated workflows for headcount management, purchase order planning, monthly accruals, and SOX audit compliance. The role will use the Databricks platform to modernize internal finance tooling and provide engineering leaders with self-service operational visibility. It will also build, mentor, and scale a high-performing finance team able to deliver both strategic capital allocation and rigorous operational execution.

Requirements

10+ years of experience in Strategic/Product Finance or R&D FP&A.
Deep experience managing large-scale R&D OpEx, headcount capacity modeling, and multi-site workforce planning within a high-growth enterprise software or cloud company.
Proven track record partnering directly with senior Engineering, Product Operations, and People/Workplace executives to drive capital allocation and operational rigor.
Systems-level thinking and demonstrated ability to re-engineer financial processes from scratch, including headcount tracking, procurement, close, and SOX controls, to support rapid scale.
Experience building, leading, and developing high-output finance teams in fast-moving environments.

Nice To Haves

Direct experience analyzing cloud dev/test infrastructure and AI tooling/token costs.
Experience attributing shared engineering and platform R&D costs across a complex, multi-product portfolio to establish product-level profitability.
Hands-on proficiency with SQL, Python, or notebook environments, especially Databricks, to automate reporting and deliver self-serve insights from large operational datasets.

Responsibilities

Establish frameworks to measure product-level ROI across the Databricks platform.
Operationalize ROI metrics to inform product investment and resource allocation decisions.
Partner with Engineering to model AI developer tooling and internal token spending alongside headcount, and quantify the tradeoff between human and compute capital.
Measure the end-to-end cost of AI-enabled engineering, including second-order effects on cloud spend, and partner with Engineering to optimize investments.
Drive multi-year headcount planning across global engineering hubs, balancing cost per hire, talent density, and university recruiting pipelines.
Partner with Workplace and Facilities leadership to align R&D hiring roadmaps with global real estate strategy and multi-year CapEx planning.
Build scalable, automated R&D finance workflows for headcount management, PO planning, monthly accruals, and SOX audit compliance.
Use the Databricks platform to modernize internal finance tooling and provide engineering leaders with self-serve operational visibility.
Build, mentor, and scale a high-performing finance team.
Partner with senior Engineering, Product Operations, and People/Workplace executives to drive capital allocation and operational rigor.