An overview of this role

As an Intermediate Backend Engineer, you'll guide the design and development of backend systems that help the company identify customer usage patterns across GitLab SaaS and Self-Managed deployments. That data informs product decisions. This role offers the opportunity to build foundational infrastructure that makes instrumentation simpler and more reliable for teams at GitLab.

What you’ll do

• Design, build, and maintain a unified Go-based instrumentation service that consolidates instrumentation across the entire company, eliminating the need for multiple language-specific SDKs while maintaining reliability and performance.
• Manage the handling of the sending, transit, and quality of instrumentation data across the system, ensuring data integrity that directly impacts the company's key prioritization and usage billing accuracy.
• Train and assist product development teams across the company on how to instrument their features using the unified service, providing documentation, guidance, and technical help.
• Manage on-call duties during working hours for systems that handle usage billing and instrumentation, ensuring system reliability and quick response to critical issues.
• Work across research and development teams and the enterprise data organization to identify requirements and deliver solutions that serve multiple stakeholders.
• Make key architectural decisions that balance the needs of product teams (who need ease of use) with data consumers (who need reliability and correctness), ensuring the system serves as a foundational service for the company.

What you’ll bring

• Proficiency in the Go programming language, with experience building and maintaining production services.
• Strong backend development experience, with the ability to design scalable, reliable systems serving internal and external customers.
• Experience with infrastructure concerns such as system reliability, performance at scale, data quality, and observability.
• Experience designing and building APIs (REST, gRPC, or similar) that other teams integrate with.
• Experience working in cross-functional teams with product teams, data consumers, and other internal stakeholders across team boundaries.
• Experience with instrumentation, analytics, data systems, or similar foundational infrastructure in application environments such as Ruby on Rails or comparable stacks.

About the team

Our team is part of the Data Engineering organization and runs a foundational service used by all research and development teams at GitLab. We manage the systems that send, transport, and validate instrumentation data across the company, giving us visibility into customer usage patterns across GitLab SaaS and Self-Managed deployment environments. This data informs usage billing and product planning. We're building a unified, Go-based instrumentation service that replaces multiple language-specific SDKs, making it easier for teams to instrument their features while ensuring data integrity for billing and analysis. As a distributed team working asynchronously across multiple time zones, we work closely with product and engineering teams across the company and provide guidance on instrumenting features effectively.

An overview of this role

As a Staff Backend Engineer, you'll set the technical direction for our instrumentation platform, guide architecture for a foundational service, and mentor engineers across the company. You'll establish patterns that help teams build reliable, scalable instrumentation that improves billing accuracy, product insights, and long-term growth.

What you’ll do

• Architect and guide the design of a unified Go-based instrumentation service, setting the technical direction to replace multiple language-specific SDKs and maintain reliable, high-performance instrumentation across the company.
• Own the strategy for handling, validating, and improving instrumentation data quality across the system, increasing billing accuracy and strengthening product planning.
• Coordinate mentorship and training initiatives with product development teams across the company, improving adoption of instrumentation best practices and raising the quality and consistency of feature instrumentation.
• Set reliability and turnaround standards for systems that enable usage billing and instrumentation, contribute to critical-incident on-call coverage, and coordinate post-incident improvements that reduce recurring issues.
• Coordinate cross-functional initiatives across research and development teams and the enterprise data organization, aligning evolving requirements and delivering solutions that serve multiple stakeholders across the company.
• Own key architectural choices that balance ease of use for product teams with reliability and correctness for data consumers, establishing patterns that scale the system as a foundational service for company-wide growth.

What you’ll bring

• Extensive production experience with Go, with a proven track record of architecting and shipping large-scale services used by multiple teams.
• Demonstrated ability to design and own backend systems that scale, with experience shaping architectural choices that influence multiple engineering teams.
• Deep expertise in infrastructure concerns including system reliability, performance optimization at scale, data quality, and observability; proven ability to set standards and mentor others in these areas.
• Proven experience designing and owning APIs (REST, gRPC, or similar) that scale to serve multiple internal teams, with the ability to evolve designs based on diverse stakeholder needs.
• Experience coordinating initiatives across functional boundaries, influencing product teams, data consumers, and other stakeholders through technical vision and mentorship.
• Deep expertise in instrumentation, analytics, data systems, or similar foundational infrastructure; demonstrated impact building systems that become critical to company operations.

About the team

Our team is part of the Data Engineering organization and runs a foundational service used by all research and development teams at GitLab. We manage the systems that send, transport, and validate instrumentation data across the company, giving us visibility into customer usage patterns across GitLab SaaS and Self-Managed deployment environments. This data informs usage billing and product planning. We're building a unified, Go-based instrumentation service that replaces multiple language-specific SDKs, making it easier for teams to instrument their features while ensuring data integrity for billing and analysis. As a distributed team working asynchronously across multiple time zones, we work closely with product and engineering teams across the company and provide guidance on instrumenting features effectively.

An overview of this role:

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 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.
• Own 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.
• Drive 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.
• Define and improve 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.
• Collaborate 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.
• Apply 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.
• Mentor and support other engineers through pairing, technical design reviews, and knowledge-sharing, reinforcing shared ownership of the system and its operational sustainability.
• Contribute 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.

What you’ll bring

• 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.
• Strong system design skills, including making and explaining clear architectural decisions, documenting constraints, and aligning trade-offs with product and platform needs.
• Strong fundamentals in preparing and structuring information for AI agents: how to curate and organize what the agent sees, design systems that support effective LLM-powered behavior, and manage context windows and token usage.
• Comfort working in ambiguous environments, with the ability to work autonomously and stay self-directed. Demonstrated ability to identify problems, drive solutions, and take ownership.
• Experience with distributed data or analytics systems (for example, OLAP databases like ClickHouse or columnar stores, Kafka‑ or NATS‑style messaging, or change data capture (CDC) pipelines), and comfort reasoning about trade‑offs in that space.
• Familiarity with graph data modeling and/or query patterns (property graphs, Cypher/GQL, n-hop traversals, aggregations), or strong interest in developing that expertise in this role.
• Practical experience using AI tools in day-to-day development, with the ability to explain how you structure prompts, validate outputs, and fold AI assistance into a disciplined engineering workflow.
• A language-agnostic mindset and evidence that you can learn new languages and frameworks as the problem requires (for example, Ruby, Go, or TypeScript/Vue in adjacent parts of the stack).
• Excellent written communication and comfort collaborating asynchronously across teams and time zones in an all-remote environment.
• Interest in helping grow others through mentoring, thoughtful code review, and sharing context as the team scales and more customers adopt Knowledge Graph.

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: each engineer is expected to build a deep understanding of the system and help evolve it over time. 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.

An overview of this role

As an Intermediate Backend Engineer on the GitLab Knowledge Graph team, you'll help build and operate a graph data service that supports GitLab Duo agents, analytics, and architecture-level features across GitLab.com, Dedicated, and Self-Managed deployments. 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.

What you’ll do

• 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).

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).

An overview of this role

As an Intermediate Backend Engineer on the GitLab Knowledge Graph team, you'll help build and operate a graph data service that supports GitLab Duo agents, analytics, and architecture-level features across GitLab.com, Dedicated, and Self-Managed deployments. 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.

What you’ll do

• 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).

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).

An overview of this role

As a Senior Backend Engineer, you'll lead the design and development of a unified, Go-based instrumentation service that consolidates analytics instrumentation across GitLab's engineering organization. You'll architect and own critical backend systems that help the company understand customer usage patterns across GitLab SaaS and Self-Managed deployment environments. That data directly informs strategic product decisions. This role offers the opportunity to build foundational infrastructure that simplifies instrumentation for every team at GitLab, replacing the complexity of multiple language-specific SDKs with a single, unified solution that all teams can rely on.

What you'll do

• Design, build, and maintain a unified Go-based instrumentation service that consolidates instrumentation across the entire company, eliminating the need for multiple language-specific SDKs while maintaining reliability and performance.
• Own the responsibility for handling the sending, transit, and quality of instrumentation data across the system, ensuring data integrity that directly impacts the company's strategic decision-making and usage billing accuracy.
• Train and support product development teams across the company on how to instrument their features using the unified service, providing documentation, guidance, and technical support.
• Manage on-call responsibilities during working hours for systems responsible for usage billing and instrumentation, ensuring system reliability and quick response to critical issues.
• Lead collaboration across research and development teams and the enterprise data organization to understand requirements and deliver solutions that serve multiple stakeholders.
• Make strategic architectural decisions that balance the needs of product teams (who need ease of use) with data consumers (who need reliability and correctness), ensuring the system serves as a foundational service for the company.

What you'll bring

• Proficiency in the Go programming language, with experience building and maintaining production services.
• Strong backend development experience, with the ability to design scalable, reliable systems serving internal and external customers.
• Experience with infrastructure concerns such as system reliability, performance at scale, data quality, and observability.
• Experience designing and building APIs (REST, gRPC, or similar) that other teams integrate with.
• Experience working in collaborative, cross-functional teams with product teams, data consumers, and other internal stakeholders across team boundaries.
• Experience with instrumentation, analytics, data systems, or similar foundational infrastructure is helpful, along with Ruby on Rails experience.

About the team

Our team is part of the Data Engineering organization and operates as a foundational service supporting all research and development teams at GitLab. We're responsible for consolidating and managing the systems that handle instrumentation data sending, transit, and quality across the entire company. Our work directly enables company strategy and decision-making by providing visibility into customer usage patterns across different product offerings and customer types (GitLab SaaS and Self-Managed deployment environments). This work supports usage billing and product decisions. We're building a unified, Go-based instrumentation service that replaces multiple language-specific SDKs, making it easier for teams to instrument their features while we ensure data integrity for billing and analysis. As a distributed team working asynchronously across multiple time zones, we collaborate closely with product and engineering teams across the company and provide training and support to help them instrument their features effectively.

An overview of this role

As an Intermediate Backend Engineer in the Database Automation team, you'll develop and enhance the frameworks, patterns, and tooling that keep GitLab's application datastores scalable, healthy, and safe across GitLab.com and thousands of self-managed instances. You'll work closely with experienced engineers and cross-functional teams to build reliable backend features, learn best practices in data architecture and lifecycle management, and contribute to identifying and addressing performance improvements in our infrastructure. As part of the Data Engineering organization, you'll gain hands-on experience with PostgreSQL and Ruby on Rails in an all-remote, highly team-based environment where you'll receive mentorship and grow your skills in data scalability, system design, and addressing the complex reliability issues self-managed customers face when upgrading and scaling.

Some examples of our projects:

• SQL Traffic Replay Tooling
• Background Operations Framework

What you’ll do

• Develop and iterate backend features and data frameworks that make it safe and efficient to work with data at scale across GitLab.com and self-managed deployments.
• Work with product management, UX, frontend, infrastructure, software delivery, and analytics teams to design and ship high-performing, reliable solutions.
• Review and improve database-related changes from other engineers and external contributors to ensure data integrity, safety, and performance across diverse environments.
• Design, build, and maintain tooling and guardrails such as SQL traffic replay and background operations frameworks to proactively detect and remediate scalability, performance, and data health issues.
• Research, design, and implement improvements to database performance, scalability, and data health, including areas like soft delete strategies and database migration testing.
• Document database best practices, anti-patterns, and data architecture guidance so developers can make informed, consistent choices.
• Develop solutions for database upgrade paths and migration strategies that maintain backwards compatibility while reducing downtime and operational friction for self-managed customers with diverse deployment configurations.
• In this role, you'll succeed by shipping incremental improvements and, over time, building the capability to fully own larger pieces of work with shorter revision cycles.

What you’ll bring

• Professional software engineering experience working with PostgreSQL in large, complex production environments, including performance tuning and troubleshooting.
• Proficiency with Ruby on Rails or another Ruby framework, with experience designing, implementing, and reviewing backend features and services.
• Ability to reason about software design, algorithms, and performance trade-offs at a system level, especially in data-intensive applications.
• Strong written communication skills and comfort working asynchronously in an all-remote, distributed team.
• Self-directed work style with effective organizational skills and the ability to manage priorities as a "manager of one."
• Alignment with GitLab's values, including transparency, teamwork, inclusion, and contribution to open source practices.
• Openness to learning and applying new tools and approaches, including transferable experience from related data, infrastructure, or developer tooling domains.

About the team

The Database Automation team is a remote, agile backend team within the Data Engineering organization. We work asynchronously across regions, partner closely with infrastructure, software delivery, and analytics, and take a self-organized approach to improving GitLab.com infrastructure while supporting self-managed customers as they upgrade and scale.

An overview of this role

As an Intermediate Backend Engineer, you'll guide the design and development of backend systems that help the company identify customer usage patterns across GitLab SaaS and Self-Managed deployments. That data informs product decisions. This role offers the opportunity to build foundational infrastructure that makes instrumentation simpler and more reliable for teams at GitLab.

What you’ll do

• Design, build, and maintain a unified Go-based instrumentation service that consolidates instrumentation across the entire company, eliminating the need for multiple language-specific SDKs while maintaining reliability and performance.
• Manage the handling of the sending, transit, and quality of instrumentation data across the system, ensuring data integrity that directly impacts the company's key prioritization and usage billing accuracy.
• Train and assist product development teams across the company on how to instrument their features using the unified service, providing documentation, guidance, and technical help.
• Manage on-call duties during working hours for systems that handle usage billing and instrumentation, ensuring system reliability and quick response to critical issues.
• Work across research and development teams and the enterprise data organization to identify requirements and deliver solutions that serve multiple stakeholders.
• Make key architectural decisions that balance the needs of product teams (who need ease of use) with data consumers (who need reliability and correctness), ensuring the system serves as a foundational service for the company.

What you’ll bring

• Proficiency in the Go programming language, with experience building and maintaining production services.
• Strong backend development experience, with the ability to design scalable, reliable systems serving internal and external customers.
• Experience with infrastructure concerns such as system reliability, performance at scale, data quality, and observability.
• Experience designing and building APIs (REST, gRPC, or similar) that other teams integrate with.
• Experience working in cross-functional teams with product teams, data consumers, and other internal stakeholders across team boundaries.
• Experience with instrumentation, analytics, data systems, or similar foundational infrastructure in application environments such as Ruby on Rails or comparable stacks.

About the team

Our team is part of the Data Engineering organization and runs a foundational service used by all research and development teams at GitLab. We manage the systems that send, transport, and validate instrumentation data across the company, giving us visibility into customer usage patterns across GitLab SaaS and Self-Managed deployment environments. This data informs usage billing and product planning. We're building a unified, Go-based instrumentation service that replaces multiple language-specific SDKs, making it easier for teams to instrument their features while ensuring data integrity for billing and analysis. As a distributed team working asynchronously across multiple time zones, we work closely with product and engineering teams across the company and provide guidance on instrumenting features effectively.