Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Staff Backend Engineer, Performance Enablement
- Remote from
- Poland
- Salary
- PLN 328k–492k / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 13 Dec 2026
- Listing views
- 14
- Application actions
- 1
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About this role.
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 a Staff Backend Engineer in GitLab’s Performance Enablement group, you’ll define and build the self-serve platform that lets GitLab engineering teams own their performance. You’ll give internal teams the tooling and guidance to run performance tests, analyze results, track trends, and meet their own performance requirements throughout the development lifecycle. We’re a platform, not a consulting service: we build capabilities that teams use independently to deliver a reliable, performant GitLab to customers. In this hands-on technical leadership role, you’ll work with the Engineering Manager to shape a multi-quarter vision and roadmap, bringing your performance engineering expertise to influence teams across research and development (R&D).
Some examples of our projects in Performance Enablement:
- Building reusable component-level testing, load testing, performance analysis, trend visibility, and artificial intelligence (AI)-assisted engineering skills
- Enabling teams to test performance throughout the development lifecycle, from local development and continuous integration (CI) workflows through pre-release validation, including modular feature teams’ own workflows
What you’ll do
- Define the team’s technical vision and multi-quarter roadmap with the Engineering Manager, identify opportunities for impact across R&D, and advocate for technical priorities with stakeholders.
- Design and build reusable platform capabilities for performance testing, analysis, reporting, and trend visibility, solving complex technical problems and shipping large capabilities with minimal guidance.
- Turn lessons from early team engagements into reusable capabilities, documentation, and onboarding, measured by each new team needing less hands-on support than the last until teams can adopt the platform and manage their performance independently.
- Coordinate with peer teams across Developer Experience, Infrastructure Platforms, and product groups to align priorities and timing.
- Lead AI-first development practices in how we build and what we ship, including AI skills and agent-assisted performance workflows.
- Mentor engineers in performance engineering, platform design, and technical decision-making.
- Serve as a performance expert for other teams and provide technical guidance on initiatives spanning multiple groups.
- Take part in on-call or operational support where your team’s ownership model requires it.
What you’ll bring
- Staff-level backend or platform engineering experience, including setting technical direction across a team and the teams around it.
- A track record of building and operating performance testing and analysis systems, with an understanding of where bottlenecks appear and how to find them.
- Experience leading or anchoring a performance enablement or platform function and achieving measurable adoption of internal tooling by influencing engineers and leaders who don’t report to you.
- Experience defining and delivering a multi-quarter technical strategy or roadmap with engineering leadership.
- An AI-first development mindset demonstrated across the software lifecycle, including shaping how a team uses AI.
- Hands-on experience with load and performance testing tools such as k6 or equivalent, Kubernetes, and Amazon Web Services, Google Cloud Platform, or similar cloud infrastructure.
- Clear written, asynchronous communication, including explaining performance trade-offs to engineers and leadership.
About the team
Performance Enablement is part of Platform Enablement within Developer Experience and Infrastructure Platforms. We work asynchronously and collaborate broadly across R&D, coordinating with peer platform and product groups. Documentation, onboarding, and feedback loops help us turn early team engagements into capabilities that scale beyond individual teams.
Poland base salary range: zł 328,000 – zł 492,000
The base salary range for this role’s listed level is currently for residents of the EU, applicable in the specified country or countries only. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location and having regard to any applicable collective agreements. The base salary range does not include bonuses, equity, or benefits.
How GitLab Supports Full-Time Employees
- Benefits to support your health, finances, and well-being
- Flexible Paid Time Off
- Team Member Resource Groups
- Equity Compensation & Employee Stock Purchase Plan
- Growth and Development Fund
- Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you’re excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.
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Sample interview questions
I would begin by identifying common workflows, service interfaces, and adoption barriers across teams. I would provide reusable test templates, CI integrations, standardized metrics, dashboards, and clear documentation, while keeping configuration flexible for team-specific needs. I would measure success through adoption, reduced manual support, test coverage, and the time required for teams to detect and resolve regressions.
I first establish a reliable baseline and define the affected user-facing metric, such as latency, throughput, or error rate. I use observability data, traces, profiles, infrastructure metrics, and controlled load tests to isolate changes across application code, databases, dependencies, and infrastructure. I then validate the suspected bottleneck with repeatable experiments and document both the remediation and prevention strategy.
I would align the roadmap with product reliability goals, known scaling risks, developer pain points, and organizational priorities. I would sequence foundational platform capabilities first, such as repeatable test execution, result storage, and trend visibility, then expand toward automated regression detection and AI-assisted workflows. Each quarter would include measurable outcomes, stakeholder checkpoints, and opportunities to adjust based on adoption feedback.
I would focus on reducing friction and demonstrating practical value through early partner teams. Clear documentation, integrations with existing developer workflows, visible success metrics, and responsive feedback loops help establish trust. I would also communicate performance trade-offs in business and engineering terms so leaders and developers understand why adoption matters.
AI can accelerate analysis by summarizing test outcomes, identifying anomalous trends, suggesting likely bottlenecks, and helping engineers create or refine test scenarios. I would use it as an assistive capability supported by reliable telemetry, guardrails, and human review rather than as an unverified decision-maker. The goal is to improve engineering speed and insight while maintaining reproducibility and technical accountability.
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