Engineering Manager, Data Foundations

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 Engineering Manager at GitLab, you’ll manage and grow a high-performing engineering team within the Data Foundations group, working on a core data platform that ingests, processes, persists, and queries data streams generated across GitLab. We are looking for a leader who can leverage AI to drive non-linear productivity gains across the platform, accelerating our ability to deliver value to our customers. We’re looking for someone with deep distributed systems knowledge. You’ll need to be comfortable going well beyond people management and into the architecture of high-throughput, multi-component data systems: ingestion, buffering, enrichment, replication, storage, querying, backpressure handling, isolation, and production operations across multiple deployment models. You’ll partner closely with Product, Design, Infrastructure, Data, and other Engineering teams to evolve a platform that lives inside the product, keeps external services to a minimum, and runs across GitLab.com, Dedicated, Self-Managed, and Cells-based deployments. In addition to Data Insights Platform, this role will take on classic search scope as the team joins the Data Foundations organization. You’ll help lead architecture and execution across both GitLab’s analytics platform and classic search capabilities, balancing platform depth with customer-facing impact. You’ll help lead architecture and execution across both GitLab’s analytics platform and classic search capabilities, balancing platform depth with customer-facing impact. In this role, you’ll balance technical guidance with people management. You’ll hire, coach, and develop engineers while also helping drive architecture and execution across a platform built around stateless ingesters, Siphon CDC replication, NATS/JetStream buffering, enrichment pipelines, ClickHouse-backed storage, and a Query API that interfaces with the GitLab Rails monolith. In Data Foundations, we build the engineering systems that make platform data reliable, scalable, and available to product teams across GitLab, and you’ll help guide that work. What you’ll do Hire, manage, and enable a high-performing Data Insights Platform engineering team, creating an environment where team members can do their best work and deliver strong results. Partner closely with product managers, product designers, and peer engineering managers to define and deliver the roadmap for Data Insights Platform (DIP) and related Platform Insights initiatives such as Siphon , Query API integrations, classic search initiatives, and self-service reporting foundations. Own delivery for your team, including planning, prioritization, execution, and operational follow-through across architecture work, platform improvements, and production readiness, with clear accountability for roadmap milestones and delivery outcomes. Guide the technical design of distributed data-path components, including ingestion, buffering, enrichment, exporting, and querying, and shape architecture choices on sharding, partitioning, component-specific scaling, failure recovery, and tenant isolation across SaaS, Dedicated