Senior Director, Warehouse Management Systems & Labor Management Systems

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.

Job Title: Senior Director, Warehouse Management Systems & Labor Management Systems

Current Need:

The Senior Director, Warehouse Management Systems (WMS) and Labor Management Systems (LMS) is a senior technology leader with end-to-end accountability for the platforms that power McKesson's distribution network. This role owns the technology strategy, modernization roadmap, delivery performance, operational reliability, and financial outcomes of systems that directly enable inventory accuracy, order fulfillment, workforce productivity, and distribution center efficiency at enterprise scale.

As the primary technology partner to Distribution Operations, Strategic Distribution & Automation, and Supply Chain leadership, this leader translates business priorities into platform outcomes — balancing operational stability with modernization, engineering excellence with delivery speed, and team development with accountability.

This role requires a leader who moves with urgency, builds process where none exists, champions AI adoption as a force multiplier for engineering productivity, and drives a culture where teams bring solutions — not problems. Candidates who thrive in ambiguity, challenge teams to work differently, and prove results will find this role uniquely rewarding.

Key Responsibilities

Enterprise Strategy & End-to-End Ownership

• Define, own, and execute the enterprise technology strategy, roadmap, and lifecycle management for WMS and LMS platforms across McKesson's distribution network.
• Maintain full end-to-end accountability — from strategy through execution — for platform availability, delivery performance, operational reliability, and financial outcomes.
• Establish clear platform principles, guardrails, and success metrics that enable disciplined, outcome-driven decision-making across engineering and operations teams.
• Align WMS and LMS strategy with distribution operations, supply chain priorities, and enterprise technology architecture to ensure investments deliver measurable business value.

Delivery Excellence & Engineering Discipline

• Drive a culture of delivery excellence — teams execute with speed, quality, and accountability in support of mission-critical distribution center operations.
• Establish clear delivery standards, remove execution bottlenecks, and enable teams to ship outcomes faster while maintaining reliability and operational stability.
• Own operational governance across distribution centers, ensuring Root Cause Analysis (RCA) and Corrective and Preventive Actions (CAPA) are delivered on time, clearly documented, and understood by both technical and non-technical leaders.
• Define and track engineering KPIs that drive accountability and continuous improvement:
• Manage portfolio of projects and reduce TCO
• System availability and uptime
• Incident reduction and mean time to resolution
• Delivery predictability and cycle time
• Code quality and technical debt reduction
• Automation coverage and engineering productivity

AI Adoption & Engineering Productivity

• Champion the use of AI coding tools — GitHub Copilot, Claude Code, and emerging platforms — to measurably increase engineering speed, reduce analysis time, and improve code quality across WMS and LMS teams.
• Model AI-first practices personally. Build a culture of experimentation where teams identify where AI helps, prove it with results, and scale what works.
• Establish repeatable, teachable examples of AI-accelerated delivery — impact analysis, code review, RCA documentation, test case generation — and actively share these across the broader engineering organization.
• Drive the expectation that AI adoption is a practice built through doing, not a training program. Teams experiment, document results, and share learnings every sprint.
• Partner with other engineering leaders to build a shared library of AI use cases that elevates productivity across the organization — not just within WMS and LMS.

Technology Modernization & Innovation

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