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Salary: USD 156,800 - 235,200 / annual
GitLab is seeking a Senior Backend Engineer for Platform Enablement to build and evolve internal platforms and tooling that enable engineering teams to own their own performance testing and analysis. You'll transition GitLab from a hands-on consulting model to a self-service model where service teams independently manage their performance tests, thresholds, and evidence.
In this role, you'll design and build reusable backend platforms and tooling for performance testing and analysis. You'll create self-service workflows, documentation, templates, and AI-assisted diagnostics that reduce the need for bespoke support. You'll use development practices that leverage AI to accelerate implementation, testing, debugging, and technical documentation. You'll influence engineering teams to make performance testing part of their normal development and release preparation workflows, and you'll turn early hands-on engagements into generalized capabilities that many teams can adopt independently.
You'll support work such as enabling modular feature teams (including Orbit and Artifact Registry) to run performance testing through their own development and CI workflows, building reusable performance tooling including component-level testing, load testing, and performance analysis, improving performance results and trend visibility so teams can make informed decisions, and establishing documentation, templates, and guidance to expand self-service adoption across R&D.
You'll provide technical expertise for difficult performance questions, tooling enhancements, and new platform capabilities. You'll work asynchronously, collaborate broadly across R&D, and focus on building capabilities that scale beyond individual team engagements.
Requirements:
- Experience building backend or platform software
- Experience building internal platforms or developer-facing tooling
- Experience partnering with development teams to improve their day-to-day workflows
- Experience using development practices that apply AI across the software lifecycle
- Experience with software performance (performance analysis, optimization, benchmarking, or leading performance-related improvements)
- Practical experience with Kubernetes, k6, and AWS, GCP, or similar cloud infrastructure
- Experience building durable solutions that support broad adoption using systems thinking
- Strong written communication and ability to influence teams without direct authority
