Principal Technical Program Manager, Continuous Improvement - Global Data Center Operations , Central Operations

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we"re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain - and we"re looking for talented people who want to help.

You"ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You"ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you"ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.

Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud powering hundreds of thousands of businesses in hundreds of countries around the world. As part of the AWS Infrastructure organization focused on building the foundation for the world"s innovation, Data Center Community (DCC) includes design, engineering, building capacity, 24/7 operations, automation, energy and sustainability investments.

About the Role

Global Data Center Operations (GDCO) is seeking a Sr. Technical Program Manager, Continuous Improvement to lead enterprise-wide process optimization across data center operations. In this role, you will apply advanced statistical methods, Lean and Six Sigma methodologies, and AI-driven analytics to eliminate waste, reduce variability, and accelerate capacity delivery at global scale.

You will serve as the single point of contact (SPOC) for process optimization across DCO - translating operational data into executive-ready insights that directly influence VP and SVP-level decisions on resource allocation, tool investment, organizational design, and capacity trade-offs. You will also design and scale a continuous improvement education program for ~100 Cluster Operations Leads building a distributed network of process-literate leaders who can identify and execute improvement opportunities within their clusters without central dependency.

This role is based in Seattle, WA and requires up to 50% travel to MLZ Data Centers nationwide

Key job responsibilities

Statistical Process Control & Advanced Analytics: Apply statistical tools and AI capabilities to isolate root causes of variability in capacity delivery, repair cycle times, and workforce productivity - transforming operational intuition into quantifiable, defensible decision inputs.

Process Optimization & Waste Elimination: Lead enterprise-wide identification and elimination of non-value-added steps across capacity delivery, repair, and logistics workflows using Lean and DMAIC methodologies to compress cycle times and reduce cost-per-rack.

VP/SVP-Level Influence & Strategic Advisory: Translate operational data into executive-ready insights and investment recommendations, directly influencing senior leadership decisions by quantifying the cost of inaction and projected ROI of process interventions.

Lean & Six Sigma Education at Scale: Design and deliver a structured curriculum covering Lean principles, Six Sigma methodology, defect elimination, waste reduction, and statistical thinking for ~100 Cluster Operations Leads

Work Complexity Reduction: Identify, quantify, and eliminate non-physical-touch work and administrative burden from technician queues through value stream mapping and statistical analysis of touch-time ratios, reallocating skilled labor capacity toward high-value hands-on delivery.

Global Process Standardization: Drive adoption of standardized workflows and directed work across all regions, ensuring consistent execution regardless of site, cluster, or operator tenure.

Capacity Delivery Velocity: Partner with delivery leaders to apply constraint theory, statistical process control, and variation analysis to install-driven bottlenecks - reducing variability and improving on-time handoff rates across core and Gen AI platforms.

Continuous Improvement Governance & Operating Model: Establish and govern the CI operating system (Kaizen events, value stream maps, tollgates, control plans, project pipelines) across GDCO, embedding improvement discipline into operational cadence rather than treating it as episodic project work.