Staff Enterprise Architect
GitLab · Remote, United States
About this role
## Staff Enterprise Architect ### About the role As a **Staff Enterprise Architect**, you’ll design and evolve how GitLab’s internal systems are configured, integrated, perform, and scale to support a rapidly growing business. You’ll report to the **Director, Enterprise Architecture and AI Intelligent Automation** and apply all four architecture lenses—**business, application, data, and technology**—with **AI as a standing consideration** across each. You’ll lead through architecture (no people management), partnering with **Applications engineering** teams and **Integration, RPA, and Intelligent Automation**, and serving as a reviewer on the **Architecture Review Board** to make architecture an enabler—not a gate. ### What you’ll do #### Business architecture - Map current-state processes and capabilities, linking each capability to supporting systems, owners, and systems of record. - Identify and resolve friction, manual handoffs, and duplicated work (including opportunities where an AI agent could remove handoffs entirely). - Architect end-to-end processes across front-office and back-office systems (e.g., **new product introduction, CPQ/quote-to-cash, billing & revenue recognition, renewals, record-to-report**) alongside customer service processes (case/escalation management, entitlements, service level commitments). - Redesign workflows before automating them—deciding what an **AI agent** handles, what **deterministic automation** handles, and what stays with a **person**. #### Application architecture - Serve as an embedded architect with Applications engineering teams, producing high-level designs and patterns for changes to **Salesforce, NetSuite, Zuora, Zendesk**, and other core platforms. - Prefer configuration and supported extension patterns over customization; evaluate each platform’s native AI before building anything bespoke. - Design for maintainability and supportability (service ownership, observability/error handling, runbooks, realistic total cost of ownership). - Stay current on release notes, limits, API behavior, and constraints so designs reflect what platforms will actually do. - Govern application change by reviewing significant build proposals, data model changes, and third-party package additions; keep platforms on supported versions and ready to upgrade. - Run quarterly health checks (e.g., customization footprint vs. API/governor limits, automation/storage/transaction/ticket volumes, AI consumption, licenses) and publish report cards with headroom and ownership for fixes. #### Data architecture - Architect data flows across operational and analytical planes, including retrieval/grounding patterns for enterprise data usable by AI. - Design data access and handling patterns to meet **privacy, residency, and SOX** requirements, including boundaries on what AI models/agents may read, retain, or act on. - Establish integration and data contracts so downstream consumers aren’t impacted by upstream schema changes. #### Technology architecture - Define platform and integration patterns (iPaaS like **Workato**, identity/access management, secrets handling, and dependent cloud services). - Define how systems expose capabilities to AI agents via APIs, tool interfaces, and scoped permissions. - Partner with Infrastructure for reliability and scale (environment strategy, sandbox/test data management, non-functional requirements). - Partner with Security for authentication/authorization and service-to-service acces
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