Revenue Analytics Manager

GitLab

1d•$126,400 - $213,600•Remote

About The Position

As a Revenue Analytics Manager, you’ll be the go-to individual contributor for turning complex sales and go-to-market data into clear insights and tools that help leaders make better decisions. You’ll work with first order, SMB, and sales development and core sales performance metrics to build AI tools, analytics, and insights that Field Operations stakeholders can rely on. You’ll own projects end to end: partnering with Sales and Operations stakeholders to understand questions, managing projects, run analysis, and translating results into recommendations that drive tangible impact. You’ll work primarily with Claude, dbt, Snowflake, SQL, and Python to build insights that can drive the business. Deliverables will be platform agnostic where you will be asked to determine the right solution for the current need, be it a scalable data model, verified queries, agentic workflows, dashboard and reporting, or even ad hoc analysis via gsheets. You’ll collaborate closely with adjacent functions such as Pipeline, Sales Strategy, and Customer Success analytics. Over your first year, you’ll focus on establishing a strong analytics foundation for new business and first orders, delivering quick-win insights and scalable reporting, and becoming a trusted partner to leaders across the go-to-market organization.

Requirements

Background working in sales analytics, with hands-on experience interpreting key sales metrics such as pipeline health, quota attainment, and forecast accuracy.
Proficiency with AI and business intelligence tools, with direct experience using Tableau or a similar BI platform and the ability to adapt to GitLab’s tooling and approach.
Practical skills in querying, and transforming data using SQL and Python, and comfort working with modern data warehouses and analytics environments.
Experience with Analytic Engineering and the ability to determine when a solution should be a view, a table, or a custom query to meet long term objectives.
Ability to manage projects and stakeholders, translating business questions into analytical plans and communicating findings to non-technical partners.
Experience supporting sales or go-to-market teams, with familiarity in areas such as new business development, pre-sales, marketing, or small and mid-market sales processes.
Exposure to tools and concepts such as dbt, Snowflake, or comparable technologies, and an understanding of data structures that support scalable reporting.
Clear, structured communication skills in an all-remote, asynchronous environment, including documenting work and collaborating across time zones.
Openness to learning GitLab’s product, data model, and ways of working, with transferable experience from sales, revenue, or field operations analytics roles.

Responsibilities

Lead end-to-end first order and new business analytics work, from defining questions with stakeholders to building datasets, analysis, and clear recommendations that inform sales decisions.
Determine which legacy Tableau dashboards need to be maintained, which need to be updated to new reporting standards, and which can be migrated to AI-based solutions.
Drive deep analysis of first order pipeline, new business attainment, 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.
Own 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.
Manage stakeholder expectations and project timelines, prioritizing requests and communicating trade-offs and outcomes in a clear, structured way.
Partner with Customer Success and broader go-to-market teams to align sales analytics with go-to-market and customer success metrics where needed.
Contribute to improving analytics processes, documentation, and standards so the team can work more efficiently and reuse high-impact analytical assets.