Senior Revenue Analytics Analyst

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. *Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Revenue Analytics Analyst, you'll be a key partner to our global Sales Strategy, Solutions Architect and Ecosystem teams, using data to improve how we engage in the pre-sales motion, tie trials of our products into consumption, and demonstrate how our Partners influence winning deals and resulting consumption. You'll translate questions from Solution Architect, Sales Strategy, and Ecosystem partners into clear analytical requirements. You'll explore customer engagement, adoption, and satisfaction data and turn findings metrics to build AI tools, analytics, and insights that Field Operations stakeholders can rely on. Working closely with Revenue Analytics teammates and cross-functional stakeholders, you'll build and maintain analytics foundations that power one-to-many engagements, digital touchpoints, and automation. You'll experiment with AI and legacy tech stack tooling to increase speed to insight. You'll also advocate for data quality, consistency, and clear definitions as you shape how we measure and understand customer health and outcomes. What you'll do - Partner with Solution Architect, Sales Strategy, Ecosystem, and other Go-To-Market stakeholders and teammates to translate questions about pre-sales engagement, consumption, pipeline, and sales efficiency into clear analytical requirements. - Design and build AI and analytic solutions that provide actionable insights for trial management, win rates, adoption, conversion, and overall sales metrics. - Craft well-structured, maintainable solutions in both business intelligence and AI tools that follow internal standards and make it easy for customer-facing teams to monitor performance and take action. - Partner with operational and data teams to define requirements for stakeholders and engagement data models, shaping how data is collected, structured, and made available for analysis. - Use segmentation, cohort analysis, and experimentation techniques, such as A/B testing, to inform scaled engagement strategies and forecast the impact of digital programs. - Serve as a subject matter expert in sales analytics by sharing best practices, documenting logic and methodologies, and providing guidance to help partners and other analysts use data to make better decisions. What you'll bring - Experience in analytics roles focused on pre-sales motions and SaaS, including analyzing customer engagement, adoption, and health across the customer lifecycle. - Background combining data from multiple customer and go-to-market systems to create a unified view of pre-sales and Partner engagements and outcomes. - Proficiency writing complex SQL queries with joins, aggregations, common table expressions, and conditional logic to support reporting and in-depth analysis. - Drive analysis of pipeline, pre-sales engagements, trial success rates, and other sales metrics using SQL and Python to uncover trends, risks, and opportunities. - Collaborate closely with Sales, Field Operations / RevOps, Finance, and Customer Success partners to translate business needs into scalable analytical solutions and tools. - Partner with teammates to define the quality, structure, and usability of sales data in partnership with central data teams, ensuring consistency across Snowflake, dbt models, and other data sources where relevant. - Ability to translate complex pre-sales and sales questions into clear analytical approaches, and to communicate findings and recommendations in a concise, accessible way to both technical and non-technical audiences. - Experience collaborating with cross-functional partner

Experience in analytics roles focused on pre-sales motions and SaaS, including analyzing customer engagement, adoption, and health across the customer lifecycle. Background combining data from multiple customer and go-to-market systems to create a unified view of pre-sales and Partner engagements and outcomes. Proficiency writing complex SQL queries with joins, aggregations, common table expressions, and conditional logic to support reporting and in-depth analysis. Drive analysis of pipeline, pre-sales engagements, trial success rates, and other sales metrics using SQL and Python to uncover trends, risks, and opportunities. Collaborate closely with Sales, Field Operations / RevOps, Finance, and Customer Success partners to translate business needs into scalable analytical solutions and tools. Partner with teammates to define the quality, structure, and usability of sales data in partnership with central data teams, ensuring consistency across Snowflake, dbt models, and other data sources where relevant. Ability to translate complex pre-sales and sales questions into clear analytical approaches, and to communicate findings and recommendations in a concise, accessible way to both technical and non-technical audiences.