The Director, Data & AI Architect, Operations and Finance will define and lead the overall Data & AI architecture strategy for Operations and Finance, ensuring alignment with enterprise priorities and business transformation goals. This role will provide senior architecture leadership across Data & AI technology capabilities, helping translate complex business needs into scalable, secure, and reusable technology patterns. The leader will partner closely with business, technology, enterprise architecture, security, compliance, and vendor teams to shape target-state architecture, guide investment decisions, and enable trusted, high-value Data & AI solutions across the Operations and Finance portfolio.
Key Responsibilities
- Own the Operations & Finance Data & AI architecture vision, strategy, and multi-year architecture roadmap; align architecture with data & AI enterprise standards, business strategy, regulatory requirements, and technology investment priorities.
- Define and establish the target operating model for Data and AI architecture across Operations and Finance, including governance, decision rights, architecture review mechanisms, reusable patterns, and clear accountability across business and technology organizations.
- Lead and develop a high-performing leadership bench of senior architects; build organizational capability, succession depth, and a scalable architecture community of practice in collaboration with enterprise architects.
- Guide enterprise tooling and partner strategy for data and AI; evaluate strategic vendors, influence sourcing decisions, and oversee complex proof-of-concept and due diligence efforts tied to long-term capability building.
- Define architecture patterns for consuming trusted enterprise data products, semantic services, knowledge graphs, and retrieval services to enable scalable AI-powered business applications.
- Contribute to financial stewardship for a large-scale architecture and transformation portfolio, including shaping annual and multi-year investment plans, cost discipline, and value realization expectations.
- Establish architecture standards for Data & AI platform and applications, including data platform, data products, MLOps, agentic orchestration, multi-agent collaboration, human-in-the-loop controls, memory management, runtime observability, guardrails, and responsible AI patterns.
- Define runtime architecture standards for scalability, resiliency, performance, monitoring, telemetry, observability, disaster recovery, and operational readiness of Data & AI applications.