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- Full Time
- 勤務形態
- Remote
- 給与
- 記載なし
この仕事について
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GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
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.
- Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As a Staff Enterprise Architect, you will design and evolve how GitLab's internal systems are configured, integrate, perform and scale to support a rapidly growing business. Reporting to the Director, Enterprise Architecture and AI Intelligent Automation, you will apply all four architecture lenses (business, application, data, and technology) to the systems in your scope, treating AI as a standing consideration inside each rather than a separate workstream.
Enterprise-wide strategy, portfolio direction, and standards ownership sit with the Director. Your job is depth: turning that direction into designs detailed enough to build, and being the person trusted with the hardest technical problems in the enterprise. You will not manage people.
You will lead through architecture, as the partner to our Applications engineering teams and to Integration, RPA, and Intelligent Automation, and as a reviewer on the Architecture Review Board, where the aim is to make architecture an enabler rather than a gate.
What you'll do
Business architecture
- In collaboration with product management and process owners, map the current-state processes and capabilities supported by the systems in your scope, tying each capability to its supporting systems, owners, and systems of record, and identify and resolve where those systems create friction, manual handoffs, or duplicated work, including handoffs an AI agent could remove entirely.
- Architect end-to-end processes that span front-office and back-office systems, including new product introduction, CPQ and quote-to-cash, billing and revenue recognition, renewals, and record-to-report, together with the customer service processes that run alongside them, including case and escalation management, entitlements, and service level commitments, rather than optimizing any one system in isolation.
- Redesign workflows before automating them, so we aren't encoding broken processes into new tooling. For each step, decide what an AI agent should handle, what deterministic automation should handle, and what stays with a person.
Application architecture
- In collaboration with the Applications engineering teams, serve as their embedded architect, producing high level designs and patterns for changes to Salesforce, NetSuite, Zuora, Zendesk, and other core platforms. Favor configuration and supported extension patterns over customization, evaluate each platform's native AI before anything bespoke is built, and keep system roadmaps aligned with enterprise standards.
- Design for maintainability and supportability: clear service ownership, observability and error handling in integrations, documented runbooks, and realistic total cost of ownership.
- Go deep on the platforms in your scope, staying current on release notes, published limits, API behavior, and known constraints, so designs account for what the platform will actually do rather than what the documentation promises.
- Govern application change by reviewing significant build proposals, data model changes, and third-party package additions before they are committed, giving teams a clear and timely decision, and keeping platforms on supported versions and ready to upgrade.
- In collaboration with each domain's technical lead, or the vendor where that is the better source, run quarterly health checks on Salesforce, NetSuite, Zuora, Zendesk, and Workato, measuring customization footprint against each platform's published ceilings for API and governor limits, automation, storage, transaction and ticket volumes, AI consumption, and licenses. Publish a report card showing headroom, what is trending toward a limit, and who owns the fix.
Data architecture
- Architect data flows across operational and analytical planes, including the retrieval and grounding patterns that make enterprise data usable by AI, alongside API-first integrations, event-driven patterns, batch pipelines, data warehouse models, and reverse ETL back into business systems.
- Design data access and handling patterns that meet privacy, residency, and SOX requirements, setting explicit boundaries on what AI models and agents may read, retain, or act on, plus field-level controls for sensitive customer and financial data.
- Establish integration and data contracts between systems, so downstream consumers aren't adversely affected by upstream schema changes.
Technology architecture
- Define platform and integration patterns for the systems in your scope, spanning iPaaS (Workato), identity and access management, secrets handling, and the cloud services those systems depend on. Define how each system exposes its capability to AI agents, through APIs, tool interfaces, and scoped permissions.
- In collaboration with Infrastructure, design for reliability and scale, covering environment strategy, sandbox and test data management, and non-functional requirements, while CI/CD and disaster recovery remain theirs to own.
- In collaboration with Security, design authentication, authorization, and service-to-service access for the systems in your scope, applying the standards they and the Director set.
- In collaboration with the enterprise AI team, recommend, use case by use case, whether a need is best served by domain-specific AI embedded in a business application or by a shared enterprise AI capability, applying their decision and model governance framework, and assemble the evidence each recommendation rests on: data access and sensitivity, reuse across functions, and cost.
- Monitor and optimize cloud and platform consumption, covering right-sizing, license and API-limit management, and eliminating spend on unused or duplicated capacity.
Across all four domains
- Produce the designs teams build from: target-state architectures, decision records, reference patterns, and high level designs for major projects and significant platform changes. Specify them in enough detail to build against: data models, field-level mappings, error and retry behavior, failure modes, and how AI output is evaluated where a design uses it. Every design must satisfy SOX, internal control, and security requirements.
- Co-author those designs with engineering teams and stay with them through build, making the call on trade-offs, resolving ambiguity in the spec, and unblocking decisions so delivery doesn't stall.
- Take the hardest technical problems in your domains personally, building proofs of concept when a decision cannot be settled on paper, including the production issues no one else has been able to root-cause.
- Collaborate very closely with adjacent technology teams, including Data, Security, and Infrastructure, so that architecture is thought through end to end and serves the whole enterprise rather than one function at a time.
- Maintain a prioritized inventory of technical debt across your domains and drive it down by attaching remediation to funded projects, upgrades, and migrations already touching that surface. Root-cause production defects rather than clearing them, fixing the design so the same class of defect stops recurring.
What you'll bring
- Deep expertise in application architecture and data architecture, with working fluency in business process and technology architecture, AI as a working lens across all four, and the judgment to know which lens a given problem needs.
- Strong hands-on background in integration, automation, and applied AI, including retrieval-augmented generation, tool calling and agent orchestration on a platform such as Claude, plus the evaluation and guardrails that make them safe to deploy, the judgment to know where AI belongs and where it does not, and fluency using AI in your own daily work, including AI-assisted development, rather than only designing it for others.
- Hands-on experience architecting core business systems, including ERP, CRM, CPQ, billing, financial, and customer service platforms, with the discipline to keep them standard, upgradeable, and SOX-compliant, plus fluency in the processes that cross them, from quote-to-cash through record-to-report.
- Expert-level knowledge of data modeling, integration architecture patterns, and API design for enterprise systems, plus practical experience with data warehouse technologies and ELT, ETL and reverse ETL pipelines, across operational and analytical use cases.
- Depth in iPaaS tooling and the operational realities of running integrations in production, including cost and consumption optimization, plus working knowledge of the cloud platforms those integrations run on.
- A track record of driving architectural change through influence rather than authority: designs that survived implementation, standards adopted on platforms owned by other teams, a clear technical position held in review forums without losing the partnership, and trade-offs explained in plain business language to Director and VP stakeholders, in writing and async.
About the team
The Enterprise Architecture team at GitLab designs, governs, and evolves the internal technology landscape that powers our business. We build with Claude across our enterprise systems, from agent-assisted workflows in our core platforms to the tooling our own team uses daily.
We set architecture direction for the Applications, Integration, and RPA teams: how core systems connect, where automation belongs in a business process, and the standards every build is held to for scale, security, and SOX compliance. We partner early in the cycle with product management, engineering teams, and system owners in Sales, Finance, Customer Success, and Operations, and we collaborate very closely with the Data, Security, and Infrastructure teams.
Together we ensure our architecture enables speed and quality for the rest of the enterprise. For more on how we work, see the Enterprise Applications Team Handbook Page .
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
$168,000 — $285,600 USD
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
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 .
主な業務内容
- Business architecture
- In collaboration with product management and process owners, map the current-state processes and capabilities supported by the systems in your scope, tying each capability to its supporting systems, owners, and systems of record, and identify and resolve where those systems create friction, manual handoffs, or duplicated work, including handoffs an AI agent could remove entirely.
- Architect end-to-end processes that span front-office and back-office systems, including new product introduction, CPQ and quote-to-cash, billing and revenue recognition, renewals, and record-to-report, together with the customer service processes that run alongside them, including case and escalation management, entitlements, and service level commitments, rather than optimizing any one system in isolation.
- Redesign workflows before automating them, so we aren't encoding broken processes into new tooling. For each step, decide what an AI agent should handle, what deterministic automation should handle, and what stays with a person.
- Application architecture
- In collaboration with the Applications engineering teams, serve as their embedded architect, producing high level designs and patterns for changes to Salesforce, NetSuite, Zuora, Zendesk, and other core platforms. Favor configuration and supported extension patterns over customization, evaluate each platform's native AI before anything bespoke is built, and keep system roadmaps aligned with enterprise standards.
- Design for maintainability and supportability: clear service ownership, observability and error handling in integrations, documented runbooks, and realistic total cost of ownership.
- Go deep on the platforms in your scope, staying current on release notes, published limits, API behavior, and known constraints, so designs account for what the platform will actually do rather than what the documentation promises.
- Govern application change by reviewing significant build proposals, data model changes, and third-party package additions before they are committed, giving teams a clear and timely decision, and keeping platforms on supported versions and ready to upgrade.
- In collaboration with each domain's technical lead, or the vendor where that is the better source, run quarterly health checks on Salesforce, NetSuite, Zuora, Zendesk, and Workato, measuring customization footprint against each platform's published ceilings for API and governor limits, automation, storage, transaction and ticket volumes, AI consumption, and licenses. Publish a report card showing headroom, what is trending toward a limit, and who owns the fix.
- Data architecture
- Architect data flows across operational and analytical planes, including the retrieval and grounding patterns that make enterprise data usable by AI, alongside API-first integrations, event-driven patterns, batch pipelines, data warehouse models, and reverse ETL back into business systems.
- Design data access and handling patterns that meet privacy, residency, and SOX requirements, setting explicit boundaries on what AI models and agents may read, retain, or act on, plus field-level controls for sensitive customer and financial data.
- Establish integration and data contracts between systems, so downstream consumers aren't adversely affected by upstream schema changes.
- Technology architecture
- Define platform and integration patterns for the systems in your scope, spanning iPaaS (Workato), identity and access management, secrets handling, and the cloud services those systems depend on. Define how each system exposes its capability to AI agents, through APIs, tool interfaces, and scoped permissions.
- In collaboration with Infrastructure, design for reliability and scale, covering environment strategy, sandbox and test data management, and non-functional requirements, while CI/CD and disaster recovery remain theirs to own.
- In collaboration with Security, design authentication, authorization, and service-to-service access for the systems in your scope, applying the standards they and the Director set.
- In collaboration with the enterprise AI team, recommend, use case by use case, whether a need is best served by domain-specific AI embedded in a business application or by a shared enterprise AI capability, applying their decision and model governance framework, and assemble the evidence each recommendation rests on: data access and sensitivity, reuse across functions, and cost.
- Monitor and optimize cloud and platform consumption, covering right-sizing, license and API-limit management, and eliminating spend on unused or duplicated capacity.
- Across all four domains
- Produce the designs teams build from: target-state architectures, decision records, reference patterns, and high level designs for major projects and significant platform changes. Specify them in enough detail to build against: data models, field-level mappings, error and retry behavior, failure modes, and how AI output is evaluated where a design uses it. Every design must satisfy SOX, internal control, and security requirements.
- Co-author those designs with engineering teams and stay with them through build, making the call on trade-offs, resolving ambiguity in the spec, and unblocking decisions so delivery doesn't stall.
- Take the hardest technical problems in your domains personally, building proofs of concept when a decision cannot be settled on paper, including the production issues no one else has been able to root-cause.
- Collaborate very closely with adjacent technology teams, including Data, Security, and Infrastructure, so that architecture is thought through end to end and serves the whole enterprise rather than one function at a time.
- Maintain a prioritized inventory of technical debt across your domains and drive it down by attaching remediation to funded projects, upgrades, and migrations already touching that surface. Root-cause production defects rather than clearing them, fixing the design so the same class of defect stops recurring.