Data Product Manager - Supply Chain | Onsite

Photon•United States,

1d•$60,000 - $210,000•Onsite

About The Position

The Data Product Manager - Supply Chain will define and maintain the roadmap for supply chain data products, aligning investments to service, inventory, productivity, cost, and member-availability objectives. This role involves running discovery with supply chain operators and leaders across various functions to understand decisions and operational constraints. The position owns prioritization across key areas such as inventory visibility, in-stock and out-of-stock insights, replenishment, DC throughput, transportation, order visibility, exception management, and network performance. Responsibilities include defining canonical business concepts, operational KPIs, business rules, event requirements, latency expectations, acceptance criteria, and data quality thresholds. The role partners with engineering, analytics, and architecture to deliver governed products on Databricks that support both analytical and operational workflows. A key aspect is building a path from descriptive visibility to predictive and prescriptive decision support, including exception alerts, demand sensing, inventory optimization, and scenario planning. The role also plans launch, workflow integration, training, support readiness, and adoption with operational users, ensuring value is realized through changes in decisions and behaviors. Additionally, the Data Product Manager will make dependencies across various departments visible and measure product performance through adoption, timeliness, quality, decision-cycle improvement, and operational outcomes.

Requirements

5+ years of relevant product management, data product, analytics product or comparable experience.
Bachelor's degree in a related field, or equivalent practical experience.
Experience owning product vision, discovery, roadmap, prioritization, requirements, launch readiness, adoption and outcome measurement.
Hands-on working knowledge of Databricks and modern lakehouse concepts, including governed data products, pipelines, semantic layers, data quality, lineage and self-service consumption.
Ability to partner effectively with data engineering, analytics, data science, architecture, security, privacy and business teams.
Ability to define product outcomes and KPIs, use evidence to make prioritization decisions and communicate complex data topics in business language.
Working knowledge of agile product delivery, backlog management, dependency planning and change adoption.
Working knowledge of supply chain language and strategy, including inventory, replenishment, forecasting, purchase orders, distribution-center operations, transportation, fulfillment, service levels and exception management.

Nice To Haves

Retail, wholesale club, grocery, distribution, logistics or omnichannel fulfillment experience.
Experience with WMS, TMS, ERP, order management, inventory, robotics or automation data.
Experience with control towers, forecasting, optimization, event-driven data products or operational AI.

Responsibilities

Defines and maintains the roadmap for supply chain data products, aligning investments to service, inventory, productivity, cost and member-availability objectives.
Runs discovery with supply chain operators and leaders in clubs, distribution centers, transportation, replenishment, fulfillment and planning to understand decisions and operational constraints.
Owns prioritization across inventory visibility, in-stock and out-of-stock insights, replenishment, DC throughput, transportation, order visibility, exception management and network performance.
Defines canonical business concepts, operational KPIs, business rules, event requirements, latency expectations, acceptance criteria and data quality thresholds.
Partners with engineering, analytics and architecture to deliver governed products on Databricks that support both analytical and operational workflows.
Builds a path from descriptive visibility to predictive and prescriptive decision support, including exception alerts, demand sensing, inventory optimization and scenario planning where justified by evidence.
Plans launch, workflow integration, training, support readiness and adoption with operational users, recognizing that value is realized only when decisions and behaviors change.
Makes dependencies across merchandising, clubs, finance, vendors, logistics systems and shared data visible before they affect committed outcomes.
Measures product performance through adoption, timeliness, quality, decision-cycle improvement and the operational outcome each product is intended to influence.