Manager, Analytics Engineering (GTM)

Company Overview

GitLab is an open-core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. When everyone can contribute, consumers become contributors, significantly accelerating human progress. Our platform unites teams and organizations, breaking down barriers and redefining what's possible in software development. Thanks to products like Duo Enterprise and Duo Agent Platform, customers get AI benefits at every stage of the SDLC.

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.

Position Overview

The Manager, Analytics Engineering (GTM) sits at the intersection of Business Strategy, Analytics Engineering, and Data Engineering and is responsible for leading a team that brings robust, efficient, and integrated data products to life. The Manager, Analytics Engineering (GTM) speaks the language of business teams and technical teams, able to translate data insights and analysis needs into data models powered by the Enterprise Data Platform. The successful Manager, Analytics Engineering (GTM) is able to blend business acumen with technical expertise and transition between business strategy and data development. In this role, the incumbent will have an opportunity to drive impact on a large scale by leading a team that delivers trusted data that is used by Senior Leadership to power GTM analytics at GitLab (Finance, Marketing and Sales).

Responsibilities

• Team Leadership: Manage, lead, and develop a high-performance Analytics Engineering team specializing in GTM data across Finance, Marketing, and Sales domains, including day-to-day assignments, bi-weekly milestone planning, 1-1s, quarterly objectives, and annual reviews.
• Data Model Design: Design and oversee the development of comprehensive data models that power critical operational reporting for Finance, Marketing, and Sales teams, enabling real-time business performance monitoring and decision-making.
• Player/Coach: Act as a player/coach that can both lead a team of expert data modelers and make hands-on contributions to GTM data model development, ensuring accurate revenue recognition, campaign attribution, and sales pipeline analytics.
• Strategic Alignment: Understand the big picture of GitLab's go-to-market strategy and demonstrate how your team's data models support revenue growth through prioritization, planning, and strategic solutioning aligned with business objectives.
• Data Pipeline Development: Build and maintain robust data pipelines that integrate complex GTM data sources (CRM, marketing automation, financial systems, sales tools) into unified, reliable operational reporting frameworks.
• Stakeholder Partnership: Partner closely with Finance, Marketing, and Sales leadership to translate evolving business requirements into scalable data model specifications that support forecasting, performance tracking, and operational excellence.
• Platform Operations: Manage operational components of the GTM Data Platform to ensure critical business metrics and operational reports are available per established SLOs, supporting month-end close, sales forecasting, and marketing ROI analysis.
• DataOps Implementation: Implement the DataOps philosophy specifically for GTM data workflows, ensuring data accuracy, consistency, and governance standards that meet the stringent requirements of financial and operational reporting.
• Standards Development: Continuously develop and enhance the Data Team Handbook's GTM data modeling standards.