Jobs / United States / Databricks

Databricks

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

Mountain View, California; San Francisco, California; Seattle, Washington Posted today

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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.
  • 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.
  • What Databricks paid sponsored hires in similar roles3 certified filings for “Sr.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
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. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-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 roles
3 certified filings for “Sr. Staff Product Operations Manager” (Operations Research Analysts) in CA: $104k–$187k, median $126k. Most were filed at wage level II (33%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

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).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

(GAQ427R231) About the role 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. The impact you will have • 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. • Build, mentor, and scale a high-performing team of finance professionals capable of operating seamlessly across strategic capital allocation and rigorous operational execution. What we look for • 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. 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

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