Data Product Manager - Merchandising | Onsite

Photon•United States,

1d•$60,000 - $210,000•Onsite

About The Position

The Data Product Manager - Merchandising role is responsible for defining the vision and roadmap for merchandising data products. This involves understanding merchant workflows, pain points, and unmet needs through direct discovery with various business teams. The role owns prioritization across competing needs such as assortment, category performance, pricing, promotions, supplier performance, item hierarchy, product content, and digital merchandising. Key responsibilities include defining business rules, product requirements, acceptance criteria, KPIs, and quality expectations for data and engineering teams. The role also partners with data governance and domain experts to establish trusted data definitions, ownership, quality thresholds, lineage, and access. A significant aspect of the role is using Databricks and the enterprise data platform to develop scalable, reusable data products, and planning for their launch and adoption beyond technical release. The role also evaluates opportunities for forecasting, optimization, recommendations, experimentation, and AI-enabled merchant decision support, while maintaining a healthy backlog and driving predictable delivery.

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 retail merchandising language and economics, including assortment, category management, pricing, promotions, item hierarchy, product lifecycle, supplier performance and margin.

Nice To Haves

Retail, wholesale club, grocery or consumer commerce experience.
Experience with merchandising analytics, demand forecasting, product information, pricing or promotion optimization.
Experience introducing AI/ML capabilities into merchant workflows and measuring adoption and value.

Responsibilities

Defines the vision and roadmap for merchandising data products and connects each investment to a stated merchandising or enterprise objective.
Runs discovery directly with merchants, category teams, pricing, planning, digital merchandising and adjacent users to understand decisions, workflows, pain points and unmet needs.
Owns prioritization across competing needs such as assortment, category performance, pricing, promotions, supplier performance, item hierarchy, product content and digital merchandising.
Defines business rules, canonical concepts, product requirements, acceptance criteria, KPIs and quality expectations clearly enough for data and engineering teams to build and validate.
Partners with data governance and domain experts to establish trusted definitions, ownership, quality thresholds, lineage and appropriate access for merchandising data.
Uses Databricks and the enterprise data platform to develop scalable, reusable data products rather than one-off reports or disconnected data extracts.
Plans launch and adoption beyond technical release, including workflow integration, training, support readiness, communications and product measurement.
Evaluates opportunities for forecasting, optimization, recommendations, experimentation and AI-enabled merchant decision support.
Maintains a healthy backlog, exposes dependencies early and drives delivery at a predictable rate while adjusting priorities when evidence or strategy changes.