Lead and develop two pods (\~8 people, including two pod leads) spanning data engineering and AI development, promoting technical excellence, speed, and AI-native thinking Define and execute the strategic roadmap for data infrastructure modernization — migrating and revamping legacy pipelines into scalable, automated systems optimized for AI consumption Own the end-to-end AI enablement strategy: building, evaluating, and deploying AI skills, agents, and evaluation frameworks that deliver trusted workforce insights at scale Uphold and advance "Agent vs.
Analyst" parity – ensuring agents reach the same conclusions an analyst would, in a manner that is reconcilable and evaluable Own the reliability layer for AI readiness: pipelines built to agent-appropriate standards for latency, format, and refresh cadence, with data quality and compliance guarantees behind every agent Drive the development and consolidation of self-service automation tools with ongoing monitoring and continuous improvement Partner with domain owners to define guidelines for core versus bespoke skill creation Implement product enhancements, including Context Library, Metric Governance, YAML configurations, and semantic layer builds, to drive AI agent accuracy Establish and maintain operating models for ownership of tables, pipelines, access control lists, skills, evaluations, and agents across DFI, WSI, and XFN partners Partner with Data Governance on metric and AI-skill certification, and ensure compliance with People data classification standards Collaborate with Workforce Management, People Analytics, Finance, and Recruiting leadership to ensure our data foundations support workforce planning, headcount management, and executive reporting needs Maintain technical depth sufficient to evaluate the direction, critically evaluate the pipeline and agent design, and hold credibility with your teams