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Photon

Builds custom software and AI for big companies

Data Product Manager - Merchandising

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 5 years of relevant product management, data product, analytics product, or comparable experience.
  • A 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 key performance indicators, 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.
  • Ability to build consensus across functions without reporting-line authority.
  • Ability to hold commitments across concurrent workstreams and communicate early when constraints require trade-offs.
  • Ability to uphold ethical, privacy, security, quality, and governance standards for data products.

Responsibilities

  • Define the vision and roadmap for merchandising data products and connect each investment to a stated merchandising or enterprise objective.
  • Run discovery with merchants, category teams, pricing, planning, digital merchandising, and adjacent users to understand decisions, workflows, pain points, and unmet needs.
  • Own prioritization across assortment, category performance, pricing, promotions, supplier performance, item hierarchy, product content, and digital merchandising needs.
  • Define business rules, canonical concepts, product requirements, acceptance criteria, key performance indicators, and quality expectations for data and engineering teams.
  • Partner with data governance and domain experts to establish trusted definitions, ownership, quality thresholds, lineage, and appropriate access for merchandising data.
  • Use Databricks and the enterprise data platform to develop scalable, reusable data products rather than one-off reports or disconnected data extracts.
  • Plan launch and adoption beyond technical release, including workflow integration, training, support readiness, communications, and product measurement.
  • Evaluate opportunities for forecasting, optimization, recommendations, experimentation, and artificial-intelligence-enabled merchant decision support.
  • Maintain a healthy backlog, expose dependencies early, and drive delivery at a predictable rate while adjusting priorities when evidence or strategy changes.
  • Own a portfolio of merchandising data products used across business and technology functions.
  • Make roadmap, scope, and sequencing decisions within the product area and recommend investment trade-offs when shared data, platform capacity, or business priorities conflict.
  • Create clarity across business, data, engineering, analytics, architecture, security, and change-management partners; make trade-offs visible; and hold the product team accountable for outcomes and adoption.
  • Conduct discovery and understand operational workflows through periodic visits to clubs, distribution centers, or other company facilities as needed.
  • Travel occasionally for product discovery, planning, implementation, and stakeholder needs.

Desired Qualifications

  • Retail, wholesale club, grocery, or consumer commerce experience.
  • Experience with merchandising analytics, demand forecasting, product information, pricing, or promotion optimization.
  • Experience introducing artificial-intelligence or machine-learning capabilities into merchant workflows and measuring adoption and value.

About the Company

Photon

Builds custom software and AI for big companies

Photon designs, builds, and runs custom software for large companies, combining an in-house creative agency with its own engineering teams. Its flagship client work includes rebuilding Samsung's US online store as a set of smaller linked services and creating Olive Garden's mobile app, which lets diners join the waitlist from their phones instead of waiting at the door. Clients hire Photon's teams to plan and code their apps, websites, and AI tools and to keep them running afterward, paying for the engineers and designers assigned to each project. Photon is privately held and based in London, with its US arm in Dallas and engineering centers in Chennai and Bengaluru.

Company size
5,001-10,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
Not listed

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