Jobs / United States / Databricks
Product Finance Director- AI Products & GPU Investment Strategy
Databricks · 🇺🇸 Mountain View, California; San Francisco, California; Seattle, Washington
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 445 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 78 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Databricks paid sponsored hires in similar roles1 certified filing for “Strategy & Execution Director” (Management Analysts) in NY: $234k–$234k, median $234k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Databricks
The US Department of Labor certified 445 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 78 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Databricks →
About the role
(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 • Establish a trusted, unified GPU demand plan across production inference, internal research, and new product bets so we invest ahead of growth without over-provisioning. • Drive capacity commitment and procurement strategies across major hyperscalers and emerging neoclouds, optimizing the mix of reserved, spot, and on-demand compute. • Own AI product economics and partner on pricing • Lead revenue and margin analytics across the AI product portfolio, turning raw consumption telemetry into a clear read on customer adoption, retention, and product profitability. • Model the end-to-end unit economics of AI products before they ship, partnering with Product and Pricing to design consumption structures that drive adoption while protecting gross margin targets. • Establish robust KPIs and operations to drive best-in-class execution • Partner with Engineering to define north star efficiency KPIs (e.g., GPU utilization, cost per token/query, serverless efficiency) and translate them into committed engineering roadmaps. • Run tight financial operations, from auditable multi-cloud cost allocation to automated reporting, giving product and engineering leaders real-time visibility into their economic goals. • Bring finance rigor to the biggest investment decisions • Design ROI and capital allocation frameworks to evaluate asymmetric bets—including frontier model research, internal compute clusters, and strategic partnerships. • Build, mentor, and scale a high-performing team of product finance analysts capable of operating at the intersection of deep technical infrastructure and commercial strategy 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, in