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Monthly
€16,933 - €28,800
Posted August 7, 2026 · 0 days agoLast seen August 7, 2026Est. expiry September 11, 2026

Director of Revenue Analytics

Senior Director, Revenue Analytics
Remote - United States
Remote · Data & Analytics
Full-time · Director
English
People Manager
How this salary compares
Salary Context: Director of Revenue Analytics

Hover or tap a row for full statistics (EUR / month on this chart).

Salary analysis

Compared with the selected benchmark ("All roles in Remote - United States"), this listing's salary midpoint is about 111% higher. The offer sits above the benchmark range (€1,667–€20,050). The listed pay band (€16,933–€28,800) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 7 comparable listings.

Monthly salary comparison for Director of Revenue Analytics
MarketLower bound (25th percentile)MedianUpper bound (75th percentile)
All roles in Remote - United States€1,667/per month€11,003/per month€20,050/per month
From job ad (Director)€16,933/per month€22,867/per month€28,800/per month
About the role

As Senior 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.

Job Details

Responsibilities

  • Lead and develop the Revenue Analytics team and set analytics standards
  • Support AI self-serve initiatives through certified data sets and semantic layers
  • Lead the development of insights using AI tooling and legacy BI solutions
  • Own the analytics roadmap for sales, customer success, and professional services
  • Develop forecasting, pipeline, capacity, and predictive models
  • Build customer lifecycle analytics for onboarding, adoption, engagement, churn risk, and NRR/GRR
  • Provide strategic leadership for the migration to a consumption-based business model
  • Create executive artifacts, integrated reporting, and attribution models
  • Partner with cross-functional executive leaders to turn business questions into recommendations
  • Establish data governance and reporting frameworks to improve data quality

Requirements

  • 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
  • Experience with consumption-based metrics and business models
  • Ability to develop strategic analytics roadmaps and influence executive-level decisions
  • Experience with customer success analytics (health scoring, churn prediction, NRR, GRR, segmentation, expansion)
  • Experience with sales and professional services analytics (forecasting, revenue planning, utilization, project profitability)
  • Ability to work with large, complex datasets for resource allocation and capacity planning
  • Advanced proficiency with AI tools (Claude, OpenAI, Gemini)
  • Advanced proficiency with BI tools (Tableau, Hex, Omni)
  • Advanced proficiency with SQL, data modeling, statistical analysis, and predictive modeling

Skills & Technologies

ClaudeOpenAIGeminiTableauHexOmniSQLData modelingStatistical analysisPredictive modelingGainsightPendo
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