What You’ll Do Lead and develop cross-functional Agile teams composed of Product Owners, Scrum Masters, Software Engineers, Quality Engineers, and Data/Analytics roles (mix of full-time and contingent). Own people leadership: coaching, development plans, performance management, hiring, team health, and building a trust-filled culture. Drive delivery leadership: sprint health, capacity planning, dependency management, continuous improvement, and predictable execution against roadmap commitments. Product Management  define and drive product strategy for the team’s products/capabilities, manage OKRs, prioritize outcomes, and align roadmap with stakeholder needs. Deliver enterprise-grade services and capabilities including:  Cloud-native, event-driven services and APIs Curated data products for analytical consumption  Knowledge agents integrated into real business workflows  Agentic capabilities: implement repeatable engineering patterns and operating practices for building, running, and evolving agents (e.g., retrieval/knowledge agents, analytical agents, workflow automation). Guide modern engineering enablement: CI/CD pipelines, infrastructure-as-code, secure-by-design principles, and scalable service patterns. Build durable stakeholder relationships across the ART and enterprise forums; communicate progress clearly, surface risks early, and unblock delivery. Manage vendor contributions and budget guardrails, ensuring delivery remains sustainable and aligned with financial expectations. You Will Be Successful In This Role By Building trust and clarity across the team: strong coaching cadence, role clarity, and visible growth plans for team members. Building trust with stakeholders through predictable execution, transparent communication, and clear roadmap/OKR ownership. Delivering tangible progress on delivering  engineered AI and agentic enterprise data capabilities while maintaining essential “run and improve” commitments. Improving delivery confidence through measurable progress in automation and quality discipline (e.g., stronger automated testing maturity and pipeline reliability). Staying close enough to the technology to guide decision-making, promote reuse, and help engineers solve complex problems—without becoming the bottleneck. Leading with curiosity, humility, and a bias toward responsible experimentation—fast learning loops with practical guardrails. 
