A
Not Specified Permanent

Bellevue, Washington · USA job

Sr. Technical Program Manager, eXperience Measurement Science (XMS)

Amazon

Bellevue, Washington

Job description

Overview

In this role you drive end-to-end employee listening programs across science, engineering, and business strategy. You will collaborate with multiple science and software teams to design, deliver, and improve listening initiatives at scale, using technical fluency to optimize data collection, scheduling, and analytics. You translate complex methodology for non-technical audiences and ensure solutions are technically sound and actionable. This position sits at the intersection of experience measurement, architecture, and cross-functional execution, offering a chance to shape how Amazon captures and uses employee insights at global scale.

Compensation / Benefits
  • health insurance
  • 401(k) matching
  • paid time off
  • parenteral leave
  • RSUs
  • sign-on payments
Responsibilities
  • Own the full lifecycle of employee listening programs from requirements to delivery and iteration
  • Develop fluency in scheduling, data collection systems, and statistical methods powering Amazon Connections
  • Create deployment documentation for surveys, including target populations, sampling, and integrations
  • Translate scientific findings into concise customer-facing documents and launch narratives
  • Identify and drive automation to reduce manual overhead in survey development and scheduling
  • Define and monitor KPIs for listening programs and use data to guide strategy
  • Represent XMS with PXT and broader Amazon businesses to enable successful outcomes
Key requirements
  • 6+ years of technical product or program management experience
  • 3+ years of direct experience working with engineering teams
  • Experience managing cross-functional programs and coordinating releases
  • Strong ability to work in ambiguity and communicate clearly
  • excellent communication and presentation
  • data-driven decision making
  • cross-functional collaboration
  • scheduling pipelines
  • data collection systems
  • statistical approaches

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