Senior Backend Engineer, Database Excellence; Ruby

Position: Senior Backend Engineer, Database Excellence (Ruby)
Git Lab is the intelligent orchestration platform for Dev Sec Ops . Git Lab 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 Git Lab 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. Git Lab 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.
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An overview of this role

As a Senior Backend Engineer with the Database Excellence group, you'll build and evolve the data frameworks and tooling that keep Git Lab's application data stores scalable, healthy, and safe across both  and thousands of self-managed instances. A primary focus for this role is building proactive data growth controls for , including retention and lifecycle management frameworks, enforcement of retention policies at schema-change time, and tooling that keeps database growth sustainable as AI-driven development accelerates how quickly data accumulates.

You'll help teams across Git Lab reason confidently about data architecture, placement, and lifecycle management, while proactively identifying saturation points in our  infrastructure and driving corrective actions with partner teams. With the team's recent move into the Data Engineering organization, you'll also shape new scalability and data health features for self-managed customers, working hands-on with PostgreSQL and Ruby on Rails in a fully remote, highly collaborative environment.

Some examples of our projects:

Data Growth Controls (retention enforcement and data lifecycle management)
SQL Traffic Replay Tooling Background Operations Framework What you'll do Develop and iterate backend features and data frameworks that make it safe and efficient for Git Lab teams to work with data at scale across SaaS and self-managed deployments.

Design and build data retention and lifecycle management frameworks, including retention policy enforcement, bulk data removal tooling, and automated identification of tables that need retention policies.

Build shift-left guardrails, such as static analysis checks and schema-change-time enforcement, that catch unsustainable data growth patterns before they reach production.

Collaborate closely with product management, UX, frontend, infrastructure, software delivery, and analytics teams to design and ship high-performing solutions

Review and improve database-related changes from other engineers and community contributors, ensuring data integrity, safety, and performance across all deployment scenarios.

Design, build, and maintain tooling such as SQL traffic replay and background operations frameworks to proactively surface and address scalability and performance issues.

Research, design, and implement improvements to data retention and lifecycle management, database performance, scalability, and data health, including areas like soft delete strategies and database migration testing.

Document database best practices, patterns to avoid, and data architecture guidance to enable developers to make informed decisions.

Develop proactive tooling and guardrails that help developers detect and remediate potential performance and data issues early in the development lifecycle.

What you'll bring

Professional software engineering experience working with PostgreSQL in large, complex production environments, including high availability solutions with automatic failover and zero-downtime data migrations as well as managing large or fast-growing datasets through approaches such as data retention,…