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GitLab is seeking a Senior Director of Revenue Analytics to lead a high-performing analytics team and own the strategic vision for sales and customer experience data across the organization. This role combines strategic thinking with operational excellence, partnering closely with Sales Strategy, Customer Success, Professional Services, Revenue Operations, Marketing, Finance, and Enterprise Data & Analytics to deliver insights that drive business outcomes.
Key responsibilities include leading and developing the Revenue Analytics team while setting analytics standards and building scalable capabilities to support GitLab's growth. You will support AI self-serve initiatives within the Field through certified datasets and coordination on semantic layers and metric definitions. The role involves owning the analytics roadmap across sales, customer success, and professional services to improve sales productivity, retention, services utilization, and revenue performance.
You will develop and support forecasting, pipeline, capacity, and predictive models to improve forecast accuracy, conversion, velocity, resource allocation, and revenue planning. Building customer lifecycle analytics for onboarding, product adoption, engagement, churn risk, expansion, NRR, and GRR to improve customer outcomes is critical. The role requires providing strategic leadership on metrics and insights necessary to help GTM functions migrate to a consumption-based business model.
Additional responsibilities include creating executive artifacts for recurring use, integrated reporting, attribution models, and strategic analyses connecting acquisition, post-sale, and services performance. You will establish data governance and reporting frameworks that improve data quality and provide reliable insights for executive, board, and external reporting.
Required experience includes leading analytics teams in sales, customer, revenue operations, business intelligence, or consulting environments. Deep knowledge of B2B SaaS business models, sales processes, and revenue metrics across the customer lifecycle is essential. Experience with consumption-based metrics, customer success analytics (health scoring, churn prediction, NRR/GRR analysis), and sales/professional services analytics is required. Advanced proficiency with AI tools (Claude, OpenAI, Gemini), BI tools (Tableau, Hex, Omni), SQL, data modeling, statistical analysis, and predictive modeling is necessary.
