Engineering Manager, Data Foundations

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.

• 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, self-managed, and Cells deployments, with a strong focus on reliability, throughput, operability, and maintainability.

• Help the team design safe and scalable integrations with the GitLab monolith, including gRPC/Protobuf-based query paths and clear ownership boundaries between DIP and product teams building user-facing GraphQL or REST endpoints.

• Drive a high bar for security, privacy, and governance in how platform data is handled, including authentication, authorization, encryption, and safe handling of data with different privacy classifications.

• Improve operational maturity across the platform, including observability, metrics, logging, readiness, capacity planning, performance monitoring, and clear runbooks for managed environments, with a focus on improving availability, throughput, latency, and time to recovery.

• Collaborate asynchronously across teams and functions to help GitLab land customer-facing reporting capabilities on top of a strong, scalable data foundation.

• Lead the design and evolution of the platform with a focus on modular architecture, ensuring systems are extensible and ready for AI-driven integrations.

• Experience managing platform, infrastructure, or data systems teams at scale, with a track record of building high-performing, values-aligned teams.

• Deep distributed systems expertise, including strong judgment on service boundaries, asynchronous pipelines, backpressure, fault tolerance, horizontal scalability, and operating multi-component systems in production.

• Strong technical background in backend and platform engineering, with the ability to guide architecture for high-throughput event pipelines and data systems.

• Experience with technologies and patterns relevant to this space, such as change data capture, event streaming or messaging systems, OLAP data stores, query-serving layers, and service-to-service APIs.

• Ability to hire, develop, and coach team members while still contributing technical guidance on complex architecture and delivery tradeoffs.

• Strong cross-functional collaboration skills, especially when ownership is split between platform teams and feature teams consuming common infrastructure or APIs.

• Experience building or operating systems across multiple deployment models, with sound judgment about the tradeoffs between GitLab.com, Dedicated, self-managed, and cell-based environments.

• Strong written communication skills and the ability to work effectively in an all-remote, asynchronous environment.

• Familiarity with search, indexing, and query-serving systems is a strong plus.

• A deep passion for reliability, customer outcomes, and engineering and operational excellence, with a proven track record of instilling these values in high-performing teams.

In Data Foundations, we enable customers to self-serve reporting with scalable architecture. Our vision is to build a comprehensive dashboards-as-a-service framework that uses AI and relies on scalable data infrastructure.

Data Insights Platform is one of our central initiatives, and we play a key role in advancing self-service reporting across GitLab. This is the foundational layer every reporting and intelligence surface at GitLab depends on — from product dashboards to Software Engineering Intelligence to the GitLab Knowledge Graph.

In addition to Data Insights Platform, the team’s scope will include classic search as part of the broader Data Foundations organization.This brings together foundational analytics and search capabilities that help customers generate insights from product data and find relevant information across GitLab.

We partner closely with product teams to turn DIP’s real-time insights, Postgres-to-ClickHouse replication, event routing, and downstream exports into dashboards, agent experiences, and customer-facing reporting.

The scale expectations are real: we handle very large event volumes, with current and projected usage in the hundreds of millions of events per day.

For more on how we work, see:

• Platform Insights Group

• Data Insights Platform architecture blueprint

• Data Insights Platform Querying API