# Product Finance Director, AI Products & GPU Investment Strategy at Databricks

> Plan GPU supply and demand, own AI product economics, and partner on pricing for the fast-growing AI business.

- Company: Databricks (databricks.com)
- Location: Mountain View, California; San Francisco, California; Seattle, Washington
- Places: Mountain View, San Francisco, Seattle
- Posted: 2026-10-15
- Level: Director
- Team: Product Finance, AI Products & GPU Investment Strategy
- Pay: $223,800—$307,650 USD
- Apply: https://databricks.com/company/careers/open-positions/job?gh_jid=8823189002
- Page: https://ye.saranatour.dev/jobs/product-finance-director-ai-products-gpu-investment-strategy-databricks-kn7dksq61h805tpv4ehzx5h8t98hcvjw

## The posting

Product Finance Director, AI Products & GPU Investment Strategy
Databricks · Mountain View, California; San Francisco, California; Seattle, Washington

(GAQ427R229)

**About the role**

AI is the defining platform shift of our generation, and Databricks is right at the center of it. We are looking for a Product Finance leader to scale the fastest growing part of our business with both speed and financial rigor. You will be the primary finance partner to our AI Engineering and Product leaders, with a mandate that spans the full arc of the AI business: planning GPU supply and demand across clouds, shaping how we price AI products, and building the unit economics that will define the margins of this category for years to come.

The impact you will have

- Plan the GPU supply and demand that powers the business
- Own AI product economics and partner on pricing
- Establish robust KPIs and operations to drive best-in-class execution
- Bring finance rigor to the biggest investment decisions

## What we look for

Required

- 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 distill massive, complex telemetry data into models and recommendations leaders act on.
- Experience building, leading, and developing high-output finance teams in fast-moving environments.

Preferred

- Direct experience operating in the AI and GPU economy, including familiarity with training/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 to self-serve insights from raw usage data.

**Pay Range Transparency**

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page [here](https://www.databricks.com/sites/default/files/2024-08/us-pay-zone-mapping.pdf).

Zone 1 Pay Range

$223,800—$307,650 USD

**Pay Range Transparency**

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth