Director, Compensation

United States (GAQ327R301) Databricks is seeking a data-driven compensation professional to lead our Compensation Analytics & Intelligence function. This role will provide centralized compensation analytics and intelligence to drive better, faster, and more consistent decision-making through the effective use of data. This is a key role within the Compensation function at Databricks. This is a builder’s role for someone who lives at the intersection of compensation, data, and strategy. You will transform enterprise-wide compensation data into insights and operational tooling, serve as the function’s go-to lead for AI and data strategy, and act as a trusted thought partner to senior leaders on the company’s most complex pay challenges — all while maintaining the highest standards for data governance, privacy, and security. In a talent market this dynamic, you will be the person who sees where the market is moving before it moves. At Databricks, we don’t believe compensation is just a number; it’s a tool to recognize that every employee is an owner and a part of our success. We are looking for a team member who brings rigor, intelligence, and world-class craft to that mission. The impact you will have: Scope of the role This is a hands-on, high-impact role that will effectively cover the below areas and the anticipated time spent on each: Company-wide program analytics (35%) — Own enterprise-wide compensation analytics: the market benchmarking process, total comp budget forecasting, and the analytical backbone for major program decisions. AI & data strategy lead (25%) — Serve as the Compensation function’s lead on AI and data strategy. Partnership with Finance (20%) — Partner with Finance teams on cash compensation budgets and spend. Collaborate on equity budget and spend modeling in close partnership with the Exec & Equity comp lead. Competitive intelligence & QBR reporting (10%) — Track market trends and spikes/cool-downs across cash, equity, and total rewards. Strategy & program design partnership (10%) — Provide thought partnership on major comp program design and company-wide strategic problems What we look for: We’re looking for someone who has the following strengths Analytics Data fluency — This is the heart of the role. Exceptional analytical skills with a proven ability to transform raw, complex data into insights, tooling, and recommendations. You see the story in the numbers and build the systems that surface it. Know your tools — Advanced capabilities required in gSheets and Excel. Strong working fluency in SQL and experience with BI / analytics platforms (e.g., Tableau, AI/BI) strongly preferred; Python and hands-on experience applying AI to comp analytics are a meaningful plus. Lead on AI — Genuine enthusiasm and sound judgment for how AI can responsibly transform compensation work, paired with a sharp instinct for data privacy, governance, and the downstream implications of how sensitive data is handled. Know your craft — Solid foundation in job architecture, comp frameworks, market pricing, year-end and midyear pay cycles, global compensation practices, and pay-for-performance design. Be rational — Ability to balance deep comp knowledge with business solutioning from a first-principles viewpoint. Operational & partnership excellence Build innovatively & scale effectively — Create frameworks and solutions that not only solve today’s issues but anticipate future needs. Operational capability — Connect the dots between systems, players, and programs to seamlessly deliver compensation analytics and tooling. Savvy collaborator & trusted advisor — Comfortable leading conversations with Finance and senior leadership, navigating at times competing priorities and opinions, and supporting sensitive, high-stakes compensation decisions with sound judgment and data-backed recommendations. 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 base 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 anticipated 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 . Zone 1 Pay Range $217,800 — $299,550 USD Zone 2 Pay Range $196,100 — $269,600 USD Zone 3 Pay Range $185,200 — $254,650 USD Zone 4 Pay Range $174,200 — $239,600 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter ,LinkedIn and Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. 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Select all that apply. * U.S. permanent resident (Green Card holder) Individual granted asylum in the U.S. Individual granted refugee status in the U.S. Individual granted citizenship in a country other than Cuba, Iran, North Korea, or Syria Individual granted permanent residency in a country other than Cuba, Iran, North Korea, or Syria None of these apply to me Not applicable (i.e., I selected “none of the above” for the prior question) Tell us about a complex dataset or operational challenge (such as a global pay-equity audit, an equity pool modeling exercise, or a comprehensive market-pricing refresh) where you built a custom tool, model, or automated workflow to extract insights and drive a major compensation decision.In your response, please walk us through:1. The specific tools, databases, or technologies you used to build this solution (e.g., advanced Excel/gSheets, SQL, Python, Tableau, or AI-assisted workflows) and why you chose them.2. How you designed the data structure to ensure scalability, and the specific safeguards you implemented to protect data privacy, governance, and sensitive employee information.3. How you translated the raw outputs of your model into a clear narrative that successfully convinced leadership to take action. * Tell us about a time when a standard, "by-the-book" compensation framework or industry benchmark simply did not fit the unique business reality of a specific team, region, or high-stakes talent situation. How did you use first-principles reasoning to design a custom solution?In your response, please address:1. The conflict between the standard comp framework (e.g., rigid pay bands, standard equity guidelines, or geographic differentials) and the pragmatic needs of the business.2. The specific data and core business logic you used to build a new, rational solution from scratch rather than just falling back on standard practices.3. How you balanced this custom solution with the need to maintain overall consistency, fairness, and cost control across the wider organization. * Describe a time when you designed and rolled out a new compensation tool, dashboard, or analytical framework (e.g., a real-time equity model, a revamped merit cycle tracker, or automated pay-equity reporting) that had to scale across the organization while navigating competing priorities from Finance or senior leadership.In your response, please walk us through:1. How you architected the tool or framework to ensure it solved immediate operational bottlenecks while anticipating future system growth, data flows, and team expansion.2. The specific technical, system, or process integrations (connecting HRIS, compensation tools, finance systems, etc.) you had to orchestrate to make the delivery seamless.3. 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