Backend Engineer, Knowledge Graph (Rust)

GitLab

11h•$115,200 - $162,000•Remote

About The Position

GitLab is hiring an Intermediate Backend Engineer for its Orbit team, within the Data Engineering organization. The role helps build and operate Knowledge Graph, a Rust backend service that creates a property graph from GitLab software development lifecycle and code data. The service uses ClickHouse, NATS JetStream, and the Data Insights Platform, and exposes secure graph queries and MCP tools for GitLab Duo agents and product features across GitLab.com, Dedicated, and Self-Managed deployments. The engineer will deliver features and improvements in defined areas while learning the broader architecture and contributing to reliability, observability, and operational readiness. Initial ownership may include parts of the SDLC indexing pipeline or query paths. The work supports analytical services that are maintainable and able to evolve as usage grows, including efforts to harden multi-tenant behavior and reduce single points of failure. The role involves asynchronous collaboration with product, data, infrastructure, security, and AI partners in an all-remote environment. It is Rust-first but may include occasional Ruby work for Rails integration and authorization, or small frontend changes related to Knowledge Graph features. GitLab expects team members to incorporate AI into their daily workflows and to use AI-assisted development responsibly, with attention to code quality and correctness.

Requirements

Professional experience building and maintaining backend systems in production, with an understanding of reliability, maintainability, and ongoing service support, such as incident response and follow-ups.
Proficiency in at least one modern backend language and strong interest in Rust; prior Rust experience or clear evidence of ability to ramp up quickly in a Rust-first, performance-sensitive codebase.
Some exposure to distributed data or analytics systems, such as OLAP databases, Kafka- or NATS-style messaging, or change data capture pipelines, or strong motivation to develop those skills.
Interest in graph data modeling and query patterns, including property graphs, multi-step (n-hop) traversals, and aggregations, and willingness to learn the tools and concepts used by Knowledge Graph.
Practical experience or strong interest using AI tools in day-to-day development, with a thoughtful approach to validating outputs and integrating AI into workflows.
A language-agnostic mindset and evidence of ability to learn new languages and frameworks as needed, such as Ruby, Go, or TypeScript/Vue.
Solid system design fundamentals for the role's level, including reasoning about trade-offs, asking questions, and aligning implementation with documented architectural decisions.
Comfort working in a low-process, high-ownership environment, taking responsibility for work, refining problem statements with teammates, and communicating progress clearly in writing across time zones in an all-remote team.

Responsibilities

Implement and iterate on backend features in the Rust-based Knowledge Graph service, including query engine changes, SDLC and code indexing flows, and API endpoints such as MCP endpoints, with guidance from senior and staff engineers.
Maintain integrations between Knowledge Graph and the GitLab platform, including work touching GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform.
Contribute to system design discussions by proposing options, raising questions, and documenting decisions, with attention to reliability, scalability, and maintainability for analytical graph workloads.
Improve operational maturity by adding or enhancing metrics, logging, runbooks, alerts, and readiness tasks, and participate in on-call rotation as appropriate for level and experience.
Collaborate asynchronously with product, data, infrastructure, security, and AI counterparts to clarify requirements, align scope, and ship features safely.
Use AI-assisted development workflows responsibly and share effective practices with the team while maintaining focus on code quality and correctness.
Participate in code reviews, knowledge-sharing sessions, and pairing.
Contribute across the stack when needed, including occasional Ruby work for Rails integration and authorization paths or small frontend changes related to Knowledge Graph features.
Take ownership of specific components or features, help improve tests and runbooks to reduce single points of failure, and contribute to sustainable service evolution.