Distinguished Data Systems Architect - Data Engineering
An overview of this role
As a Distinguished Data Systems Architect, you will define and drive the evolution of GitLab’s strategic data platforms across both SaaS and self-managed deployments. You’ll architect scalable, distributed data systems that balance OLTP and OLAP performance, cost, and resiliency while enabling secure, compliant data use in complex, regulated environments. You’ll bring together ingestion, orchestration, transformation, and metadata into a cohesive, event-driven architecture using tools like Argo, Airflow, Kubernetes, Trino, Postgres, and graph-based metadata systems, and you’ll establish opinionated design principles, governance frameworks, and semantic models that power analytics, AI, and monetizable data products. Partnering closely with product and engineering leadership, you’ll transform ambiguous business needs into strategic technical roadmaps, embed AI-driven patterns and standards into our data infrastructure, and create a model-driven architecture that clearly separates logical data design from physical implementations to reduce technical debt and unlock new capabilities for GitLab customers and internal teams.
Some examples of our projects:
Creating a unified architectural blueprint for GitLab’s data ecosystem that aligns SaaS and self-managed platforms on shared patterns and standards
Designing monetizable data services and APIs with strong governance and observability to support new product offerings and revenue streams
What you’ll do
Lead the architectural vision for scalable, distributed data systems across GitLab’s SaaS and self-managed deployments, balancing OLTP and OLAP performance, scalability, and cost-efficiency
Design and evolve enterprise data governance frameworks, including lineage, data quality controls, versioning, and compliance practices that align with global regulatory needs
Architect monetizable data services and APIs with clear semantic models that support internal analytics and external product offerings while meeting security and performance service-level expectations
Create and maintain a cohesive architectural blueprint for GitLab’s data ecosystem, identifying gaps against modern platforms and defining opinionated design principles grounded in cloud-native patterns
Design event-driven and end-to-end data lifecycle architectures, covering ingestion, orchestration with tools like Argo, Airflow, and Kubernetes, transformation workflows, and unified metadata management with strong observability
Partner with product, engineering, and security leadership to embed AI-driven patterns into data infrastructure, align senior engineering leaders on shared design tenets, and drive platform standards adoption
Translate ambiguous business and product challenges into strategic technical roadmaps, leading high-impact architectural engagements where data platforms provide measurable competitive advantage
Design and implement a Model Driven Architecture framework that cleanly separates conceptual and logical data models from physical implementations, improving agility and reducing technical debt across enterprise data systems
What you’ll bring
Proven experience architecting large-scale, distributed data systems in complex, regulated environments, spanning SaaS and self-managed deployments
Background designing multi-modal data services and APIs with strong developer experience principles, including monetization, governance, and data product lifecycle management
Hands-on proficiency with modern data platforms and tools such as Python, Docker, Airflow, Trino, Postgres, distributed query engines, and graph-based metadata systems
Advanced understanding of data architecture concepts, including logical and conceptual modeling, Model Driven Architecture, schema management, and data processing paradigms across synchronous and asynchronous patterns
Experience building unified data platforms that integrate cloud-native compute, orchestration, and semantic modeling, bridging cloud and on-premises environments with automation and developer self-service in mind
Familiarity with modern data and telemetry standards such as OpenTelemetry, OpenMetadata, and OpenLineage, and the ability to apply them in real-world platform designs
Practical experience with AI-driven architectures and emerging technologies, including model orchestration, agentic patterns, and standards like Model Context Protocol (MCP)
Ability to lead through influence, mentor senior technical partners, and collaborate across product, engineering, and business teams, with openness to candidates who bring transferable experience from adjacent large-scale data or platform roles
About the team
Data Engineering and Monetization is a function within the Engineering organization with a mission to build a responsible, scalable data foundation that powers GitLab’s SaaS and self-managed offerings. The team brings together data engineers, architects, and platform specialists working asynchronously across regions to design, operate, and evolve unified data platforms, governance, and monetization capabilities for GitLab’s AI-powered DevSecOps platform. You’ll partner closely with product, security, infrastructure, and finance teams to tackle challenges such as harmonizing data across deployments, enabling compliant usage in regulated markets, and creating reliable, monetizable data services that unlock new product and revenue opportunities.
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
United States Salary Range
$219,100—$328,700 USD
About the job
Apply for this position
Distinguished Data Systems Architect - Data Engineering
An overview of this role
As a Distinguished Data Systems Architect, you will define and drive the evolution of GitLab’s strategic data platforms across both SaaS and self-managed deployments. You’ll architect scalable, distributed data systems that balance OLTP and OLAP performance, cost, and resiliency while enabling secure, compliant data use in complex, regulated environments. You’ll bring together ingestion, orchestration, transformation, and metadata into a cohesive, event-driven architecture using tools like Argo, Airflow, Kubernetes, Trino, Postgres, and graph-based metadata systems, and you’ll establish opinionated design principles, governance frameworks, and semantic models that power analytics, AI, and monetizable data products. Partnering closely with product and engineering leadership, you’ll transform ambiguous business needs into strategic technical roadmaps, embed AI-driven patterns and standards into our data infrastructure, and create a model-driven architecture that clearly separates logical data design from physical implementations to reduce technical debt and unlock new capabilities for GitLab customers and internal teams.
Some examples of our projects:
Creating a unified architectural blueprint for GitLab’s data ecosystem that aligns SaaS and self-managed platforms on shared patterns and standards
Designing monetizable data services and APIs with strong governance and observability to support new product offerings and revenue streams
What you’ll do
Lead the architectural vision for scalable, distributed data systems across GitLab’s SaaS and self-managed deployments, balancing OLTP and OLAP performance, scalability, and cost-efficiency
Design and evolve enterprise data governance frameworks, including lineage, data quality controls, versioning, and compliance practices that align with global regulatory needs
Architect monetizable data services and APIs with clear semantic models that support internal analytics and external product offerings while meeting security and performance service-level expectations
Create and maintain a cohesive architectural blueprint for GitLab’s data ecosystem, identifying gaps against modern platforms and defining opinionated design principles grounded in cloud-native patterns
Design event-driven and end-to-end data lifecycle architectures, covering ingestion, orchestration with tools like Argo, Airflow, and Kubernetes, transformation workflows, and unified metadata management with strong observability
Partner with product, engineering, and security leadership to embed AI-driven patterns into data infrastructure, align senior engineering leaders on shared design tenets, and drive platform standards adoption
Translate ambiguous business and product challenges into strategic technical roadmaps, leading high-impact architectural engagements where data platforms provide measurable competitive advantage
Design and implement a Model Driven Architecture framework that cleanly separates conceptual and logical data models from physical implementations, improving agility and reducing technical debt across enterprise data systems
What you’ll bring
Proven experience architecting large-scale, distributed data systems in complex, regulated environments, spanning SaaS and self-managed deployments
Background designing multi-modal data services and APIs with strong developer experience principles, including monetization, governance, and data product lifecycle management
Hands-on proficiency with modern data platforms and tools such as Python, Docker, Airflow, Trino, Postgres, distributed query engines, and graph-based metadata systems
Advanced understanding of data architecture concepts, including logical and conceptual modeling, Model Driven Architecture, schema management, and data processing paradigms across synchronous and asynchronous patterns
Experience building unified data platforms that integrate cloud-native compute, orchestration, and semantic modeling, bridging cloud and on-premises environments with automation and developer self-service in mind
Familiarity with modern data and telemetry standards such as OpenTelemetry, OpenMetadata, and OpenLineage, and the ability to apply them in real-world platform designs
Practical experience with AI-driven architectures and emerging technologies, including model orchestration, agentic patterns, and standards like Model Context Protocol (MCP)
Ability to lead through influence, mentor senior technical partners, and collaborate across product, engineering, and business teams, with openness to candidates who bring transferable experience from adjacent large-scale data or platform roles
About the team
Data Engineering and Monetization is a function within the Engineering organization with a mission to build a responsible, scalable data foundation that powers GitLab’s SaaS and self-managed offerings. The team brings together data engineers, architects, and platform specialists working asynchronously across regions to design, operate, and evolve unified data platforms, governance, and monetization capabilities for GitLab’s AI-powered DevSecOps platform. You’ll partner closely with product, security, infrastructure, and finance teams to tackle challenges such as harmonizing data across deployments, enabling compliant usage in regulated markets, and creating reliable, monetizable data services that unlock new product and revenue opportunities.
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
United States Salary Range
$219,100—$328,700 USD