Product Finance Director- AI Products & GPU Investment Strategy

Databricks•Mountain View, CA

8h•$201,400 - $307,650

About The Position

Databricks is seeking a Product Finance leader to scale its AI business with speed and financial rigor. The role is the primary finance partner to AI Engineering and Product leaders and spans GPU supply and demand planning across clouds, AI product pricing, and the unit economics and margins of the AI product portfolio. The position also builds and leads a high-performing team of product finance analysts.

Requirements

10+ years of experience in Strategic/Product Finance, complemented by foundational training in Investment Banking, Private Equity, or Management Consulting.
Deep fluency in consumption-based business models, public cloud infrastructure costs, unit cost analysis, and gross margin optimization.
A proven track record of partnering directly with senior Engineering and Product executives to influence technical roadmaps and commercial pricing.
Demonstrated ability to build scalable financial systems from scratch and turn complex telemetry data into actionable models and recommendations.
Experience building, leading, and developing high-output finance teams in fast-moving environments.

Nice To Haves

Direct experience in the AI and GPU economy, including familiarity with training and inference workloads, hyperscaler commitment structures, and neocloud economics.
Ownership of a multi-product P&L within a high-growth enterprise SaaS, PaaS, or data infrastructure company.
Hands-on proficiency with SQL, Python, or notebook environments for self-serving insights from raw usage data.

Responsibilities

Establish a unified GPU demand plan across production inference, internal research, and new product bets, balancing investment ahead of growth with avoiding over-provisioning.
Drive capacity commitment and procurement strategies across hyperscalers and neoclouds, optimizing the mix of reserved, spot, and on-demand compute.
Lead revenue and margin analytics across the AI product portfolio, using consumption telemetry to assess customer adoption, retention, and product profitability.
Model end-to-end AI product unit economics before launch and partner with Product and Pricing on consumption structures that support adoption and gross margin targets.
Partner with Engineering to define efficiency KPIs, such as GPU utilization, cost per token or query, and serverless efficiency, and translate them into engineering roadmaps.
Establish financial operations, including auditable multi-cloud cost allocation and automated reporting, to provide product and engineering leaders with visibility into economic goals.
Design ROI and capital allocation frameworks for investments such as frontier model research, internal compute clusters, and strategic partnerships.
Build, mentor, and scale a high-performing team of product finance analysts.