Staff Backend Engineer, Knowledge Graph (Rust)

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.

What You’ll Do

• Lead Design & Evolution: Drive the 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.

• Own Cross-Cutting Initiatives: Manage complex projects 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.

• Drive System Design: Make architectural decisions that improve reliability, scalability, and maintainability for analytical (OLAP‑style) graph workloads (multi‑hop traversals, aggregations, multi‑tenant isolation). Document trade‑offs to keep the team aligned.

• Improve Operational Maturity: Define and enhance Service Level Objectives (SLOs), observability, runbooks, incident response, capacity planning, and production readiness (PREP) for GitLab.com, Dedicated, and Self-Managed deployments.

• Collaborate Asynchronously: Work with product, data, infrastructure, security, and AI teams to sequence work, unblock platform‑level dependencies, and safely land features.

• Leverage AI Responsibly: Apply AI‑assisted development workflows responsibly (using MCP‑aware tools, agents, and internal Duo tooling) and help establish practical norms for the team.

• Provide Mentorship: Support other engineers through pairing, technical design reviews, and knowledge-sharing, reinforcing shared ownership of the system.

• Contribute Across the Stack: Step in when needed, including occasional Ruby (Rails integration) or frontend work (e.g., Software Architecture Map UI) to keep delivery moving.

What You’ll Bring

• Significant experience building and operating production backend systems, with a track record of owning reliability, maintainability, and on-call readiness.

• Strong engineering skills in Rust or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive backend codebase.

• Strong system design skills, including making clear architectural decisions, documenting constraints, and aligning trade-offs with product needs.

• Solid fundamentals in structuring information for AI agents (managing context windows, token usage, and organizing what the agent sees).

• Comfort working autonomously in ambiguous environments, identifying problems, and driving solutions.

• Experience with distributed data or analytics systems (e.g., ClickHouse, columnar stores, Kafka/NATS messaging, or CDC pipelines).

• Familiarity with graph data modeling and/or query patterns (property graphs, Cypher/GQL, n-hop traversals), or a strong interest in learning.

• Practical experience using AI tools in day-to-day development, with the ability to explain prompt structures and validate outputs.

• A language-agnostic mindset with the ability to learn new frameworks quickly (e.g., Ruby, Go, TypeScript/Vue).

• Excellent written communication skills for asynchronous collaboration in an all-remote environment.

(Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. If you're excited about this role, please apply and allow our recruiters to assess your application.)

About the Team

The Knowledge Graph team sits within the data engineering organization and builds the backend service that turns GitLab's SDLC and code data into a unified property graph. We expose it through a high-performance, ClickHouse-backed query engine and MCP tools.

We're a small group of senior engineers working closely with partners across AI (Duo Agent Platform), analytics, infrastructure, delivery, and security because our work touches many layers of the platform. We work asynchronously and value strong ownership. As we grow adoption, we're focused on scaling the service sustainably and making it reliable and easy to operate for GitLab.com, Dedicated, and Self-Managed customers.

Compensation & 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

• Home office support