Description
As a Staff Backend Engineer on the GitLab Knowledge Graph team, you'll help design, scale, and operate a high-impact graph data service that underpins agents, analytics, and architecture-level features across GitLab.com, Dedicated, and Self-Managed deployments.
You'll partner with a small, senior Rust-first team to ship reliable graph capabilities and make them easy for other teams and agents to use. The Knowledge Graph service is a distributed SDLC indexing system. It builds a property graph from GitLab SDLC (software development lifecycle) and code data using ClickHouse, NATS JetStream, and the Data Insights Platform. It also exposes secure graph queries and MCP tools for AI agents and product features.
In this role, you'll own core parts of the system end to end: shaping the architecture, hardening multi-tenant behavior and performance, and making it straightforward for other teams and agents to consume graph capabilities. In your first year, you'll take clear ownership of major areas of the service (for example, the graph query engine, SDLC indexing, or multi-tenant authorization), reduce single points of failure through better runbooks and shared context, and raise the bar on how we design, build, and operate analytical services across the stack.
Key responsibilities include:
- Leading the design and evolution of core Knowledge Graph services in a production Rust codebase, including the graph query engine, SDLC and code indexing pipelines, and API/MCP surfaces that other GitLab teams and AI agents rely on.
- Owning complex, cross-cutting initiatives that span GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform, from technical direction and design docs through implementation, rollout, and iteration.
- Driving system design decisions that improve reliability, scalability, and maintainability for analytical (OLAP-style) graph workloads. This includes multi-hop traversals, aggregations, and multi-tenant isolation. Document trade-offs so the broader team can move quickly and stay aligned.
- Defining and improving operational maturity for the service, including service level objectives (SLOs), observability, runbooks, incident response, capacity planning, and production readiness (PREP) for GitLab.com, Dedicated, and Self-Managed deployments.
- Collaborating asynchronously with product, data, infrastructure, security, and AI teams to sequence work, unblock platform-level dependencies, and land features in a way that is safe for customers and sustainable for the team.
- Applying AI-assisted development workflows responsibly (for example, using MCP-aware tools, Knowledge Graph-backed agents, and internal Duo tooling) and help establish practical norms for how the team uses AI while maintaining strong engineering judgment.
- Mentoring and supporting other engineers through pairing, technical design reviews, and knowledge-sharing, reinforcing shared ownership of the system and its operational sustainability.
- Contributing across the stack when needed, including occasional Ruby (Rails integration and authorization paths) or frontend work (for example, the Software Architecture Map UI) to close gaps and keep delivery moving.
This role requires significant experience building and operating production backend systems, with a track record of owning reliability, maintainability, and on-call readiness for services that support other product teams or platforms. Strong engineering skills in Rust or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive backend codebase are essential. Additionally, strong system design skills, including making and explaining clear architectural decisions, documenting constraints, and aligning trade-offs with product and platform needs, are necessary.