Data Product Manager - Merchandising

Photon; Boston (MA); On-site; USD 120,000 - 160,000; Full time

Job summary

BJ's Wholesale Club is seeking a Data Product Manager for Merchandising to own the vision, roadmap, and requirements of our merchandising data products on the enterprise data platform.

You will run discovery with merchants and cross-functional teams, define KPIs, data quality and adoption, and partner with data engineering, analytics, architecture, security and governance to deliver scalable, trusted data capabilities.

Qualifications

• 5+ years in product/data/analytics role.
• Bachelor's degree or equivalent practical experience.
• Experience owning vision, discovery, roadmap, prioritization, requirements, launch readiness and adoption.
• Hands-on Databricks knowledge and lakehouse concepts.
• Strong cross-functional partnership with data and security teams.
• Define outcomes and KPIs; communicate data topics to business.
• Experience with agile product delivery and backlog management.
• Knowledge of merchandising economics: assortment, pricing, promotions, lifecycle, margins.

Responsibilities

• Define vision and roadmap; tie investments to objectives.
• Conduct discovery with merchants and cross-functional teams.
• Prioritize across assortment, pricing, promotions, supplier performance.
• Define rules, requirements, acceptance criteria, KPIs and quality expectations.
• Partner on data definitions, quality, lineage and access.
• Leverage Databricks to build scalable data products.
• Plan launch, adoption, training and measurement.
• Explore forecasting, optimization, AI-enabled support.
• Maintain backlog and manage delivery pace.

Skills

Product management

Data product

Analytics product

Agile delivery

Stakeholder communication

Education

Bachelor's degree

Tools

Databricks

Job description

Job Title : Data Product Manager - Merchandising

Data Product Managers decide what gets built and why for BJ's merchandising data products. This role owns the vision, roadmap, prioritization and requirements that turn merchandising problems into trusted, reusable data capabilities on the enterprise data platform. It runs the work through a discovery-to-outcome cycle, from understanding merchant decisions and economics through launch, adoption and measurement. The role is accountable for business outcomes, product adoption, data trust and time to value, not for the volume of dashboards or requirements produced.

Description for Internal Candidates

Key accountabilities / essential functions

• 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.

Scope, impact and decision making

Owns a portfolio of merchandising data products used across business and technology functions. Makes roadmap, scope and sequencing decisions within the product area and recommends investment trade-offs where shared data, platform capacity or business priorities conflict. Impact is measured through adoption, data quality, time to insight and movement in the merchandising outcomes each product is designed to support.

Problem solving and complexity

Merchandising decisions combine incomplete evidence, seasonal or promotional effects, complex hierarchies and competing commercial objectives. The role must distinguish reusable domain capabilities from local reporting requests, resolve inconsistent definitions and choose what to build when several stakeholders have legitimate claims on the same capacity.

Leadership and influence

Leads through product vision, evidence, facilitation and influence rather than direct authority. Creates clarity across business, data, engineering, analytics, architecture, security and change-management partners; makes trade-offs visible; and holds the product team accountable for outcomes and adoption.

Key relationships and communication

• Merchandising and category teams: discovery, product direction, KPI definition, prioritization and adoption.
• Pricing, promotions, planning, finance and digital teams: shared measures, decision workflows, trade-offs and cross-domain dependencies.
• Data engineering, analytics, data science and architecture: product requirements, feasibility, data models, quality, lineage and delivery sequencing.
• Information security, privacy and governance: access, retention, control and responsible AI requirements.

Required qualifications

• 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.

Ways of working

• Keeping member and business value the deciding factor when scope pressure would cut it first.
• Building consensus across functions that have no reporting line to each other.
• Changing direction when evidence shows an approach is not working rather than continuing because of sunk cost.
• Holding commitments across concurrent workstreams and communicating early when constraints require a trade-off.
• Holding data products to ethical, privacy, security, quality and governance standards under pressure to ship.

Preferred qualifications

• 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.