Product Finance Director, R&D Capital Allocation & Engineering Operations
Databricks · Mountain View, California; San Francisco, California; Seattle, Washington · Finance · listed October 16, 2026
The shape of it
Seniority
Director
Experience asked
10+ years
Where
Not stated
Stated pay
$223,800 – $307,650 USD
Requirements listed
10
Length
913 words
In the posting’s own words
As Databricks scales at unprecedented velocity, how we invest our R&D capital—both human and compute—is critical to our scale. We are looking for a Product Finance leader to own R&D capital allocation and engineering operations. You will be a key finance partner to our senior Engineering and Product Operations leaders, with a mandate that spans product-level R&D ROI, global workforce and location strategy, and the rapidly evolving economics of AI-enabled software development.
What it asks for · 10
- Required
- 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.
- A proven track record of partnering directly with senior Engineering, Product Operations, and People/Workplace executives to drive capital allocation and operational rigor.
- Systems-level thinker with a demonstrated ability to re-engineer financial processes from scratch—spanning headcount tracking, procurement, close, and SOX controls—to support rapid scale.
- Experience building, leading, and developing high-output finance teams in fast-moving environments.
- Preferred
- 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 self-serve insights from large operational datasets.
What the job covers
- Drive product-level ROI and R&D capital allocation
- Establish frameworks to measure product-level ROI across a fast-moving platform like Databricks where engineering velocity is extremely high.
- Operationalize ROI metrics so they directly inform product investment and resource allocation decisions.
- Define engineering economics in the AI era
- Partner with Engineering to model how AI developer tooling and internal token spend scale alongside headcount, quantifying the tradeoff between human capital and compute capital.
- Measure the end-to-end cost of AI-enabled engineering, including the second-order impact on cloud spend, and partner with Engineering to optimize investments.
- Shape global workforce and location strategy
- Drive multi-year headcount planning across global engineering hubs, balancing cost per hire, talent density, and university recruiting pipelines.
- Partner closely with Workplace and Facilities leadership to synchronize R&D hiring roadmaps with global real estate strategy and multi-year CapEx planning.
- Scale R&D finance operations and build the team
- Bring systems-level thinking to R&D finance operations, building scalable, automated workflows across headcount management, PO planning, monthly accruals, and SOX audit compliance.
- Dogfood the Databricks platform to modernize internal finance tooling and deliver self-serve operational visibility to engineering leaders.
Tools and skills named
Data
- Databricks4×
Languages
- Python
- SQL
Operations & finance
- Recruiting
Security & compliance
- Audit
Words the posting leans on
- engineering11×
- finance8×
- capital7×
- scale6×
- experience5×
- headcount5×
- operations5×
- capital allocation4×
- cost4×
- global4×
- operational4×
- partner4×
- planning4×
- product4×
- cloud3×
- drive3×
Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.
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