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.

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You'll join a small, Rust-first team that values clear ownership, thoughtful system design, and rigorous thinking about data and reliability. The Knowledge Graph service is a Rust backend that builds a property graph from GitLab’s software development lifecycle (SDLC) and code data. It uses ClickHouse, NATS JetStream, and the Data Insights Platform. It exposes secure graph queries and MCP tools used by AI agents and product features.

In this role, you’ll deliver features and improvements in well-scoped areas, learn the broader architecture, and contribute to reliability, observability, and operational readiness. In your first year, you’ll take clear ownership of specific components or features (for example, parts of the SDLC indexing pipeline or query paths). You’ll help reduce single points of failure with better tests and runbooks, and you’ll help the team ship analytical services that are easier to maintain and evolve over time.

Responsibilities:

• Implement and iterate on backend features in the Rust-based Knowledge Graph service, including changes to the query engine, SDLC and code indexing flows, and API endpoints (including MCP endpoints) under guidance from senior and staff engineers.

• Help maintain integrations between Knowledge Graph and the rest of the GitLab platform, working in areas that touch 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 a focus on reliability, scalability, and maintainability for analytical graph workloads.

• Improve the operational maturity of the service by adding or enhancing metrics, logging, runbooks, alerts, and small readiness tasks, and by participating in on-call rotation as appropriate for your level and experience.

• Collaborate asynchronously with product, data, infrastructure, security, and AI counterparts to clarify requirements, align on scope, and ship features safely for customers and sustainably for the team.

• Use AI-assisted development workflows responsibly (for example, using Knowledge Graph-backed agents and internal Duo tooling), and share what works with the team while keeping a strong focus on code quality and correctness.

• Participate in code reviews, knowledge-sharing sessions, and pairing to both learn from others and help maintain consistent standards across the codebase.

• 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 (for example, Software Architecture Map UI plumbing).

Requirements:

• Professional experience building and maintaining backend systems in production, with an understanding of reliability, maintainability, and how to support services over time (incident responses, and follow-ups, etc).

• Proficiency in at least one modern backend language and strong interest in Rust, with either prior Rust experience or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive codebase.

• Some exposure to distributed data or analytics systems (for example, OLAP databases, Kafka- or NATS-style messaging, or change data capture (CDC) pipelines), or strong motivation to develop those skills in this role.

• Interest in graph data modeling and query patterns (property graphs, multi-step (n-hop) traversals, aggregations), and willingness to learn the tools and concepts used in Knowledge Graph over time.

• Practical experience (or strong interest) using AI tools in day-to-day development, along with a thoughtful approach to validating outputs and integrating AI into your workflow.

• A language-agnostic mindset and evidence that you can pick up new languages and frameworks as needed (for example, Ruby, Go, or TypeScript/Vue where the work touches adjacent systems).

• Solid fundamentals in system design for your level, including the ability to reason about trade-offs, ask good questions, and align your implementation work with documented architectural decisions.

• Comfort working in a low-process, high-ownership environment where you take responsibility for your work, communicate progress clearly, and help refine problem statements with your teammates.

• Strong written communication and comfort collaborating asynchronously across time zones in an all-remote team.

About the team:

We sit within the Data Engineering organization. We're a small group of senior engineers and we work closely with partners across AI (Duo Agent Platform), analytics, infrastructure and delivery, and security because our work spans many parts of the platform. We collaborate asynchronously and optimize for strong ownership rather than a feature factory model. We each build a meaningful understanding of the system and help evolve it over time. A key challenge for us right now is scaling sustainably. That includes hardening multi-tenant behavior, maturing observability and readiness, and keeping the system healthy and maintainable as usage grows and team members take time off. At the same time, we're bringing Knowledge Graph to general availability (GA).

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You'll join a small, Rust-first team that values clear ownership, thoughtful system design, and rigorous thinking about data and reliability. The Knowledge Graph service is a Rust backend that builds a property graph from GitLab's software development lifecycle (SDLC) and code data. It uses ClickHouse, NATS JetStream, and the Data Insights Platform. It exposes secure graph queries and MCP tools used by AI agents and product features.

In this role, you'll deliver features and improvements in well-scoped areas, learn the broader architecture, and contribute to reliability, observability, and operational readiness. In your first year, you'll take clear ownership of specific components or features (for example, parts of the SDLC indexing pipeline or query paths). You'll help reduce single points of failure with better tests and runbooks, and you'll help the team ship analytical services that are easier to maintain and evolve over time.

Key responsibilities include:

Implementing and iterating on backend features in the Rust-based Knowledge Graph service, including changes to the query engine, SDLC and code indexing flows, and API endpoints (including MCP endpoints) under guidance from senior and staff engineers.

Helping maintain integrations between Knowledge Graph and the rest of the GitLab platform, working in areas that touch GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform.

Contributing to system design discussions by proposing options, raising questions, and documenting decisions, with a focus on reliability, scalability, and maintainability for analytical graph workloads.

Improving the operational maturity of the service by adding or enhancing metrics, logging, runbooks, alerts, and small readiness tasks, and by participating in on-call rotation as appropriate for your level and experience.

Collaborating asynchronously with product, data, infrastructure, security, and AI counterparts to clarify requirements, align on scope, and ship features safely for customers and sustainably for the team.

Using AI-assisted development workflows responsibly (for example, using Knowledge Graph-backed agents and internal Duo tooling), and sharing what works with the team while keeping a strong focus on code quality and correctness.

Participating in code reviews, knowledge-sharing sessions, and pairing to both learn from others and help maintain consistent standards across the codebase.

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 (for example, Software Architecture Map UI plumbing).

What you'll bring:

Professional experience building and maintaining backend systems in production, with an understanding of reliability, maintainability, and how to support services over time (incident responses, and follow-ups, etc).

Proficiency in at least one modern backend language and strong interest in Rust, with either prior Rust experience or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive codebase.

Some exposure to distributed data or analytics systems (for example, OLAP databases, Kafka- or NATS-style messaging, or change data capture (CDC) pipelines), or strong motivation to develop those skills in this role.

Interest in graph data modeling and query patterns (property graphs, multi-step (n-hop) traversals, aggregations), and willingness to learn the tools and concepts used in Knowledge Graph over time.

Practical experience (or strong interest) using AI tools in day-to-day development, along with a thoughtful approach to validating outputs and integrating AI into your workflow.

A language-agnostic mindset and evidence that you can pick up new languages and frameworks as needed (for example, Ruby, Go, or TypeScript/Vue where the work touches adjacent systems).

Solid fundamentals in system design for your level, including the ability to reason about trade-offs, ask good questions, and align your implementation work with documented architectural decisions.

Comfort working in a low-process, high-ownership environment where you take responsibility for your work, communicate progress clearly, and help refine problem statements with your teammates.

Strong written communication and comfort collaborating asynchronously across time zones in an all-remote team.

About the team:

We sit within the Data Engineering organization. We're a small group of senior engineers and we work closely with partners across AI (Duo Agent Platform), analytics, infrastructure and delivery, and security because our work spans many parts of the platform. We collaborate asynchronously and optimize for strong ownership rather than a feature factory model. We each build a meaningful understanding of the system and help evolve it over time. A key challenge for us right now is scaling sustainably. That includes hardening multi-tenant behavior, maturing observability and readiness, and keeping the system healthy and maintainable as usage grows and team members take time off. At the same time, we're bringing Knowledge Graph to general availability (GA).

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

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