An overview of this role

As a Data Engineer at GitLab, you'll build, run, and maintain the pipelines and infrastructure that bring data from our enterprise applications, internal systems, and GitLab's software as a service (SaaS) platform into our data platform. Your work will power company reporting, data science, and artificial intelligence (AI) self-service.

You'll own features end to end, from source integration to tested, monitored, documented data ready for use. You'll focus on keeping the platform reliable, secure, and cost-efficient as data volumes and AI usage grow, and making governed data ready for AI tools, agents, and Model Context Protocol (MCP)-based access. This is an AI-first role: you'll use AI coding agents every day while maintaining high standards for code review and testing. You'll report to the Director, Data Platform.

What you’ll do

• Build, extend, and maintain extract, load, transform (ELT) pipelines from enterprise applications such as Salesforce, Zuora, Zendesk, and Marketo, internal systems, and the GitLab SaaS platform. Integrate data through application programming interfaces (APIs) and event sources such as Snowplow.
• Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes.
• Write maintainable, well-tested, performant Python and Structured Query Language (SQL) code that meets our internal standards, and review GitLab team members' merge requests.
• Own day-to-day platform operations: triage and resolve failed pipelines, infrastructure issues, and GitLab team member requests. Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
• Partner with Analytics Engineers, Data Analysts, Data Scientists, Data Governance, and business partners so data lands in the shape they need for downstream use.
• Use AI coding agents such as Claude Code, OpenCode, GitLab Duo, or similar tools to speed up development, debugging, testing, and documentation without compromising review rigor.
• Apply role-based access control (RBAC) and masking policies to make governed data available to AI tools and agents. Handle data subject to Sarbanes-Oxley (SOX) controls, material nonpublic information (MNPI), and personal data safely.
• Ship medium-sized features independently in small, iterative merge requests, and document architecture and runbooks in the handbook.

What You'll Bring

• Professional experience shipping production data pipelines in Python.
• Deep, hands-on SQL skills and experience with a cloud data warehouse; Snowflake is preferred.
• Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion.
• Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
• Experience running and troubleshooting data infrastructure, with comfort using Git-based workflows, continuous integration and continuous delivery (CI/CD), and code review.
• Hands-on experience using AI coding tools in your daily work, with an understanding of where they help and where they fall short.
• Clear written communication, the ability to use GitLab, and comfort working asynchronously in a fully remote organization in line with GitLab's values.
• Preferred, not required: experience with Snowflake; Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform; SaaS application APIs such as Salesforce, Zuora, or Zendesk; data observability tooling; or Snowflake administration.

About the team

The Data Platform team within Enterprise Data is responsible for a scalable, reliable and secure data platform for data analytics at GitLab. That includes managing and maintaining the infrastructure we use, both SaaS and self-managed, and building and maintaining the data pipelines that bring data into it. We make data reliable, secure and ready for analytics, data science and AI tools. As more questions go straight to AI tools and agents, the platform underneath has to be more reliable, better governed and more cost-efficient than ever. We're fully remote and globally distributed, and work asynchronously through issues, merge requests and the handbook.