Staff Enterprise Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Enterprise Architect based in United States. This role will shape the architecture, integration, performance, and scalability of critical enterprise systems supporting a rapidly growing organization. You will work across business, application, data, and technology architecture, with AI considered as an integral part of each discipline. As an individual contributor, you will tackle complex technical challenges while turning enterprise strategy into practical designs that engineering teams can build and maintain. You will partner closely with Applications, Integration, Data, Security, Infrastructure, and business stakeholders to create scalable and secure solutions. The role combines deep technical expertise with strong business judgment, architectural governance, and hands-on problem solving. It offers broad influence across core business platforms, enterprise integrations, automation, data, and emerging AI capabilities. Accountabilities: - Map current-state business processes and capabilities across systems, identifying friction, manual handoffs, duplicated work, and opportunities for AI-enabled improvements. - Architect end-to-end processes spanning areas such as product introduction, CPQ, quote-to-cash, billing, revenue recognition, renewals, record-to-report, customer service, case management, entitlements, and service commitments. - Redesign workflows before automating them, determining which activities should be handled by AI agents, deterministic automation, or people. - Serve as an embedded architect for Applications engineering teams, creating high-level designs and architectural patterns for core CRM, ERP, billing, customer service, and integration platforms.

- Favor maintainable, supported configuration and extension approaches over unnecessary customization while evaluating native AI capabilities before introducing bespoke solutions. - Govern significant application changes, data model changes, third-party packages, platform versions, and other architectural decisions to ensure scalability, supportability, security, and upgrade readiness. - Conduct regular platform health checks covering customization, API and governor limits, automation, storage, transaction and ticket volumes, AI consumption, licensing, and available capacity. - Design enterprise data flows across operational and analytical environments, including APIs, event-driven integrations, batch pipelines, data warehouses, ELT/ETL, reverse ETL, and AI retrieval and grounding patterns. - Establish secure data access and handling patterns that address privacy, residency, SOX, internal control, and sensitive customer and financial data requirements. - Define integration and data contracts to protect downstream systems and consumers from disruptive upstream changes. - Establish technology and integration patterns spanning iPaaS, identity and access management, secrets management, cloud services, APIs, AI agent tool interfaces, and scoped permissions. - Partner with Infrastructure and Security teams on reliability, scalability, authentication, authorization, service-to-service access, environments, sandbox strategy, test data, and non-functional requirements. - Collaborate with enterprise AI stakeholders to determine whether use cases are best addressed through embedded domain-specific AI or shared enterprise AI capabilities, supported by clear evidence around data access, sensitivity, reuse, and cost. - Monitor and optimize cloud and platform consumption, including right-sizing, licensing, API usage, and elimination of unused or duplicated capacity. - Produce target-state architectures, decision records, reference patterns, and high-level designs detailed enough for engineering teams to implement, including data models, field mappings, error handling, retries, failure modes, and AI evaluation approaches.