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Salary: USD 168,000 - 285,600 / annual
GitLab is seeking a Staff Enterprise Architect to design and evolve internal systems that support a rapidly growing business. Reporting to the Director of Enterprise Architecture and AI Intelligent Automation, you will apply business, application, data, and technology architecture lenses to systems in your scope, with AI as a standing consideration across all four domains.
You will not manage people. Instead, you will lead through architecture as a trusted partner to Applications engineering teams, Integration, RPA, and Intelligent Automation groups, and serve as a reviewer on the Architecture Review Board. Your focus is depth: turning strategic direction into designs detailed enough to build and being the person trusted with the hardest technical problems in the enterprise.
Key responsibilities include:
**Business Architecture**: Map current-state processes and capabilities, identify friction points and manual handoffs, architect end-to-end processes spanning front-office and back-office systems (new product introduction, quote-to-cash, billing, renewals, record-to-report, case management), and redesign workflows before automating them—deciding what AI agents should handle, what deterministic automation should handle, and what stays with people.
**Application Architecture**: Serve as embedded architect for Salesforce, NetSuite, Zuora, Zendesk, and other core platforms. Produce high-level designs and patterns, favor configuration over customization, evaluate native AI capabilities, and keep system roadmaps aligned with enterprise standards. Design for maintainability and supportability with clear service ownership and observability. Stay current on platform release notes, limits, and constraints. Govern application change by reviewing significant build proposals and data model changes. Run quarterly health checks measuring customization footprint against platform limits and publish report cards showing headroom and trending issues.
**Data Architecture**: Architect data flows across operational and analytical planes, including retrieval and grounding patterns for AI. Design data access and handling patterns meeting privacy, residency, and SOX requirements, setting explicit boundaries on what AI models may read or act on. Establish integration and data contracts between systems.
**Technology Architecture**: Define platform and integration patterns spanning iPaaS (Workato), identity and access management, secrets handling, and cloud services. Define how systems expose capability to AI agents. Collaborate with Infrastructure on reliability and scale. Work with Security on authentication, authorization, and service-to-service access. Recommend whether needs are best served by domain-specific AI or shared enterprise AI capabilities. Monitor and optimize cloud and platform consumption.
**Across all domains**: Produce target-state architectures, decision records, reference patterns, and high-level designs detailed enough to build from. Co-author designs with engineering teams and stay through build, making trade-off calls and unblocking decisions. Take the hardest technical problems personally, building proofs of concept when needed. Collaborate closely with Data, Security, and Infrastructure teams. Maintain a prioritized inventory of technical debt and drive it down by attaching remediation to funded projects.
**Requirements:**
- 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 domains
- Strong hands-on background in integration, automation, and applied AI, including retrieval-augmented generation, tool calling and agent orchestration (e.g., Claude); evaluation and guardrails for safe deployment; fluency using AI in daily work including AI-assisted development
- Hands-on experience architecting core business systems (ERP, CRM, CPQ, billing, financial, customer service platforms) with discipline to keep them standard, upgradeable, and SOX-compliant; fluency in processes crossing them (quote-to-cash, record-to-report)
- Expert-level knowledge of data modeling, integration architecture patterns, and API design for enterprise systems; practical experience with data warehouse technologies and ELT/ETL/reverse ETL pipelines
- Depth in iPaaS tooling and operational realities of running integrations in production, including cost and consumption optimization; working knowledge of cloud platforms
- Track record of driving architectural change through influence rather than authority: designs that survived implementation, standards adopted on platforms owned by other teams, clear technical positions held in review forums without losing partnership, trade-offs explained in plain business language to Director and VP stakeholders in writing and async
