Distinguished Data Systems Architect, Data Engineering at GitLab

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Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

328700.0

Posted On

20 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Architecture, Data Governance, Data Systems, APIs, Cloud-Native, Event-Driven Architecture, Data Lifecycle Management, AI-Driven Patterns, Model Driven Architecture, Data Quality Controls, Distributed Systems, Data Integration, Observability, Compliance, Data Transformation, Metadata Management

Industry

IT Services and IT Consulting

Description

GitLab is an open-core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. When everyone can contribute, consumers become contributors, significantly accelerating human progress. Our platform unites teams and organizations, breaking down barriers and redefining what's possible in software development. Thanks to products like Duo Enterprise and Duo Agent Platform, customers get AI benefits at every stage of the SDLC. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. 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 How GitLab will support you 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 Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.

Responsibilities

Lead the architectural vision for scalable, distributed data systems across GitLab’s SaaS and self-managed deployments. Design and evolve enterprise data governance frameworks and architect monetizable data services and APIs.