Director, Revenue Analytics

An overview of this role
As Director, Revenue Analytics, you'll lead a high-performing team of analytics professionals and own the strategic vision and execution for sales and customer experience data and analytics across GitLab. You'll serve as a key business partner to executive leadership, using data to shape GitLab's go-to-market strategy, customer experience, and revenue growth throughout the customer lifecycle.

This role combines strategic thinking with operational excellence. You'll partner closely with Sales Strategy, Customer Success, Professional Services, Revenue Operations, Marketing, Finance, and Enterprise Data & Analytics to deliver insights that drive business outcomes.

What you’ll do
Lead and develop the Revenue Analytics team, setting analytics standards and building scalable capabilities to support GitLab's growth.
Support AI self-serve initiatives within the Field through certified data sets, coordination with Ent Data on semantic layers and metric definitions.
Lead a team building insights via AI tooling and solutions along with legacy BI solutions.
Own the analytics roadmap across sales, customer success, and professional services to improve sales productivity, retention, services utilization, and revenue performance.
Develop and support forecasting, pipeline, capacity, and predictive models to improve forecast accuracy, conversion, velocity, resource allocation, and revenue planning.
Build customer lifecycle analytics for onboarding, product adoption, engagement, churn risk, expansion, NRR, and GRR to improve customer outcomes.
Provide strategic leadership on the metrics, insights, and data necessary to help the GTM functions migrate to a consumption-based business model.
Create executive artifacts to be used on recurring basis, integrated reporting, attribution models, and strategic analyses that connect acquisition, post-sale, and services performance.
Partner with executive leaders and teams across Revenue Operations, Marketing, Finance, Product, Professional Services, and Enterprise Data & Analytics to turn business questions into clear recommendations.
Establish data governance and reporting frameworks that improve data quality and provide reliable insights for executive, board, and external reporting.
What you’ll bring
Experience leading analytics teams and developing team members in sales, customer, revenue operations, business intelligence, or consulting environments.
Knowledge of business-to-business software-as-a-service business models, sales processes, and revenue metrics across the customer lifecycle.
Experience with consumption-based metrics and business models.
Ability to develop strategic analytics roadmaps, translate complex data into clear insights, and influence executive-level decisions.
Experience with customer success analytics, including health scoring, churn prediction, NRR and GRR analysis, segmentation, and expansion analytics.
Experience with sales and professional services analytics, including forecasting, revenue planning, utilization, project profitability, and services-led growth metrics.
Ability to work with large, complex datasets to develop analytical frameworks that inform resource allocation, capacity planning, and customer outcomes.
Advanced proficiency with AI tools (Claude, OpenAI, Gemini), Business Intelligence tools (Tableau, Hex, Omni), Structured Query Language (SQL), data modeling, statistical analysis, and predictive modeling for customer analytics.
Familiarity with customer data platforms, product analytics tools such as Gainsight or Pendo, services management systems, and other business intelligence platforms is a plus.

Skills Required
Experience leading analytics teams in sales, customer, revenue operations, business intelligence, or consulting environments.
Knowledge of B2B SaaS business models, sales processes, and revenue metrics across the customer lifecycle.
Experience with consumption-based metrics and business models.
Ability to develop strategic analytics roadmaps and translate complex data into executive-level insights.
Experience with customer success analytics (health scoring, churn prediction, NRR, GRR, segmentation, expansion analytics).
Experience with sales and professional services analytics (forecasting, revenue planning, utilization, project profitability).
Ability to work with large, complex datasets to inform resource allocation, capacity planning, and customer outcomes.
Advanced proficiency with AI tools (Claude, OpenAI, Gemini), BI tools (Tableau, Hex, Omni), SQL, data modeling, statistical analysis, and predictive modeling.
Familiarity with customer data platforms, product analytics tools such as Gainsight or Pendo, services management systems, and other BI platforms.
Experience creating executive artifacts, integrated reporting, attribution models, and governance frameworks for executive, board, and external reporting.