Senior Manager Compensation R&D

Databricks is looking for a strategic compensation leader who likes to build. You'll be a trusted partner to founders and senior leaders in our Engineering organization. Together you'll create compensation solutions that help us attract, retain and motivate world-class engineering and technical talent, and you'll use AI to build the tools that make those solutions scale. This role reports to the Director of Compensation.

At Databricks, compensation is more than a number. It's how we show that every employee is an owner and part of our success. We're looking for someone with a strong foundation and a track record of doing exactly that.

Databricks is a deeply technical, fast-moving company, and our Engineering organization builds the platform everything else runs on. You'll partner with highly technical, data-driven leaders who expect rigorous analysis, clear points of view and collaborative problem-solving. Priorities can change quickly, and you'll often run several important initiatives at once.

We set high standards for this role to support our fast-paced growth. Successful team members in this environment tend to:

• Build trust through analytical rigor. You present well-reasoned models and effectively articulate complex analysis to engineering leaders who will test your assumptions.
• Navigate fast-paced priorities with resilience. You maintain focus, sound judgment, and positivity when balancing competing deadlines and feedback.
• Offer constructive perspectives. You confidently partner with senior stakeholders to propose data-backed alternative solutions and drive alignment to finalize decisions.
• Embrace feedback as a growth tool. You extract actionable insights from feedback and respond with clarity and openness.
• Create order from ambiguity. When there's no playbook, you build one, and you're comfortable acting on 80% of the information.

If you are excited by high standards, technical depth, and collaborative strategic discussions, you will find this work deeply rewarding.

This is a hands-on, high-impact role. Expected time on each area:

• Work directly with Engineering leaders to diagnose talent challenges, such as competition for AI/ML and infrastructure talent, hard-to-fill specialist roles, rapid team scaling, acquisitions and reorganizations. Then design attraction and retention solutions that solve them.
• Turn vague or urgent requests into clear options with explicit tradeoffs, and help leaders reach a decision.
• Serve as the main compensation advisor on strategic technical hires (senior ICs, distinguished engineers, research leaders and engineering executives) and on high-stakes retention cases.
• Advise on technical leveling, including IC and management career paths and how compensation should differ across them.

• Run merit, refresh equity and promotion cycles end to end for global R&D teams.
• Design and build AI tools and workflows that reduce manual work and improve decisions. Examples include offer modeling, retention-risk analysis, cycle quality checks, and self-serve insights for leaders.
• Spot what's repetitive or error-prone in today's processes and automate it.

• Help scale Databricks' global compensation structure, including market pricing models, pay-for-performance design, equity strategy and technical job architecture.
• Build frameworks that solve today's problems and anticipate the ones coming as we grow, including emerging skill premiums such as AI/ML.

Quantitative and technical skill is the foundation of this role. Everything else we look for builds on it.

• Expert-level analytical modeling. You build compensation models from scratch, including cost-of-program scenarios, pay-positioning analysis, attrition and retention-risk modeling, and sensitivity and tradeoff analysis. 
• Built with AI. You've personally used AI tools (for example LLMs, AI assistants, or automation and agent platforms) to build compensation tools, models or workflows that others rely on. You don't need to be a software engineer, but you should be able to explain how it works, what you'd improve, and how you checked that its outputs were accurate.
• Equity fluency. You understand RSU and option mechanics, vesting design, refresh strategy, and how grant values and share price movement affect cost and retention, especially for long-tenured, high-equity technical talent.
• Statistical judgment. You know when a market data cut is credible, when a sample is too small (common with niche technical roles), and how to present ranges and market pricing without false precision.

• You know your craft. Job architecture, technical career ladders, compensation frameworks, market pricing, year-end and mid-year pay cycles, global compensation practices, and pay-for-performance design.
• You know the competition. Deep knowledge of competitor compensation for high-tech talent, especially new-hire and refresh equity strategy at high-growth tech companies, public tech companies, and AI labs.
• You reason from first principles. You balance compensation best practice with what the business actually needs, and you can rebuild a framework from its underlying logic when the standard approach doesn't fit.

• Trusted advisor to senior leaders. You have a track record of advising executives on sensitive, high-stakes compensation decisions (offers, promotions, leveling, retention, transfers, org changes) and influencing the outcome through data and judgment rather than authority.
• Operational excellence. You connect the dots between systems, stakeholders and programs to deliver compensation programs end to end, accurately and on time.
• Clear communicator. You turn complex analysis into a crisp recommendation an executive can act on in one conversation.

• Experience supporting Engineering, R&D or other technical organizations at a high-growth tech company.
• Familiarity with the market for AI/ML, research or specialized infrastructure talent.
• Experience with acquisition (including acqui-hire) compensation and retention design. 
• Experience with Tableau or other BI tools, or building dashboards for leaders.
• Experience scaling compensation programs through hypergrowth, IPO, or major organizational change.
• A background in a quantitative field (for example finance, statistics, engineering), or equivalent experience.