Data Product manager – Supply Chain

Data Product Manager – Supply Chain

The Data Product Manager – Supply Chain owns the development and delivery of high-impact data products that give the business the visibility, control, and predictive power to run a more efficient, resilient supply chain. This role collaborates with product managers, analytics and business across supply chain operations, distribution centers, transportation, planning and allocation, and the data and AI capabilities that make them smarter.

Sitting at the intersection of Product, Supply Chain, Distribution Center and Transportation Operations, Data Engineering, and Analytics, this role translates fragmented, siloed operational data (WMS, TMS, SAP, EDW, and manual spreadsheets) into a consolidated, governed data model and a roadmap of reusable, production-ready data products — spanning DC and yard operations, freight and carrier performance, planning and allocation, and exception management.

The ideal candidate brings hands-on experience in supply chain, logistics, or distribution operations within retail or wholesale — with a genuine understanding of how data drives decisions across the buy-to-shelf journey: inbound and outbound flow, inventory positioning, carrier and vendor performance, and service levels. They know Databricks and enterprise data, they know agile delivery, and they are a doer who ships.

How This Role Works

• Own the end-to-end prioritization and delivery of data products supporting distribution center operations, transportation, and planning & allocation.

• Partner with Supply Chain Product managers, business and analytics to surface high-value data opportunities and convert them into scalable capabilities.

• Define and drive the supply chain data product roadmap — bringing a value lens to every prioritization decision: what improves in-stock performance, reduces cost, minimizes waste, increases end-to-end visibility and increases user value of data.

• Play product role in delivery in an agile model — managing in-take and prioritization, partnering with engineering on delivery, holding the team to outcomes over activity and maintaining strong comms and relationships with business stakeholders.

• Balance speed with scalability — move fast to prove value on priority use cases (e.g., exception alerts, dwell time reporting, self-service insights), then harden successful capabilities into reusable, production-grade assets on the Enterprise Data Platform (EDP).

Success here is measured by the impact of shipped data products: faster time to insight, fewer manual workarounds, better decisions in DC and transportation operations, and AI/ML capabilities (forecasting, exception detection, optimization) with a strong bias towards incremental delivery, measure and learn.

Operating Principles

• Ship it:
Delivery is the job. Plans don't create value — shipped products do.

• Value first:
Every decision connects back to service levels, cost to serve, and end-to-end supply chain visibility.

• Care about the user:
Know the problem you're solving. Stay close to what DC managers, transportation planners, and allocators need — and let that drive what gets built.

• Be data-driven:
Know your KPIs, know the use cases you enable, and build products that put others in the driver's seat.

• Build for reuse: A consolidated, governed supply chain data model and scalable data products — not bespoke pipelines or one-off dashboards per request.

• AI in production:
Get models out of notebooks and into decisions. Understand where AI creates real leverage (forecasting, exception detection, routing) and deliver it.

Core Responsibilities

The Data Product Manager – Supply Chain is responsible for delivering data products that:

• Drive decisions in distribution centers — inventory levels, case/pallet lifecycle, labor productivity, and DC-to-club in-stock visibility.

• Enable transportation and logistics intelligence — inbound/outbound carrier movement, freight cost management, and vendor/carrier score carding.

• Support planning and allocation — demand forecasting, assortment and item cluster performance, and end-of-life/salvage optimization.

• Bring AI and ML into production — demand forecasting,…