Senior Workforce Planning Strategist, AWS Workforce Planning

About the role

AWS Workforce Planning (WFP) exists to translate the vision of enabling leaders to make data-driven workforce decisions with speed and clarity into reality. We are looking for an exceptional Technical Program Manager (TPM) with strategic expertise and a strong delivery record in building trust to join our team as a Sr. WFP Strategist.

Responsibilities

• Serve as the workforce planning expert in the room – bringing benchmarking, research, and quantitative rigor that earns trust and shapes how leaders think about their workforce.
• Translate complex business strategy into actionable workforce plans addressing headcount, composition, and capability requirements across the assigned org portfolio.
• Partner with Finance, HR, Talent Acquisition, and business leadership to manage creation of tools and processes that support workforce plans in cadence with AWS' operational cycles.
• Develop scalable solutions and playbooks that equip partners and leaders to execute workforce planning with greater independence.
• Build, debug, and improve automated solutions that reduce manual recurring work; leverage AI tools and workforce planning platforms to increase throughput.
• Contribute to shared knowledge infrastructure. Developing agentic solutions to share workforce planning expertise with many stakeholders.

Qualifications

• 7+ years of technical product or program management experience
• Experience using customer insights and data to deeply understand target customers and dive deep
• Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams
• Experience in managing and troubleshooting network, or experience that includes strong analytical skills, attention to detail, and effective communication abilities
• Experience setting technical requirements for process automation
• Strong analytical and problem-solving skills, expertise in deriving insights through data analysis, practical knowledge and experience in inferential statistical application