Product Manager, Data @ SKIMS

SKIMS is a solutions-oriented brand creating the next generation of underwear, loungewear, shapewear, and essentials. We are setting new standards by delivering technically innovative, comfort-driven products with elevated design for everybody. At the intersection of culture, innovation, and commerce, SKIMS is a global brand redefining modern apparel. Our teams are built on creativity, collaboration, and operational excellence, and we are committed to fostering an inclusive, high-performance environment where ambitious, curious, and values-driven individuals can do the best work of their careers. As we continue to scale globally, our HQ teams play a critical role in shaping strategy, building world-class infrastructure, and driving sustainable growth across markets.

• SKIMS is seeking a Product Manager, Data to shape and deliver data products that make trusted, actionable information available across the business.
• This role will translate business needs into a clear data product strategy, roadmap, and prioritized backlog.
• The Product Manager will focus on scalable, reusable capabilities rather than one-off reporting solutions, ensuring data investments support meaningful business decisions.
• The ideal candidate combines product management discipline, technical fluency, and strong business judgment.
• They will partner closely with Data Engineering, Analytics, Data Science, and teams across Digital, Retail, Merchandising, Planning, Finance, Marketing, Operations, and Technology.
• Data Product Strategy and Roadmap Define and maintain a data product roadmap aligned with SKIMS’ strategic and operational priorities.
• Identify opportunities to create reusable data assets, standardized metrics, and self-service capabilities across business functions.
• Prioritize investments based on business value, user needs, technical dependencies, and long-term scalability.
• Define clear product outcomes, success measures, and decision criteria for roadmap initiatives.
• Balance immediate stakeholder needs with sustainable platform and product development.
• Product Discovery and Requirements Partner with business stakeholders to understand workflows, decisions, pain points, and unmet data needs.
• Translate ambiguous requests into product requirements, user stories, acceptance criteria, and clear definitions of success.
• Facilitate alignment on business rules, metric definitions, source-system requirements, and intended user experiences.
• Validate proposed solutions with users before and during development.
• Communicate priorities, trade-offs, scope, and timelines clearly across technical and business teams.
• Delivery and Product Lifecycle Own the product backlog and partner with Data Engineering, Analytics, and other delivery teams to execute against the roadmap.
• Coordinate dependencies across data sources, integrations, models, semantic layers, and reporting experiences.
• Lead business validation, user acceptance testing, release readiness, and stakeholder enablement.
• Monitor delivered capabilities and drive enhancements based on usage, feedback, and changing business needs.
• Identify delivery risks early and support pragmatic decisions that protect product quality and value.
• Data Quality, Governance and Trust Champion consistent business definitions, clear ownership, documentation, and discoverability of data products.
• Partner with technical teams to define expectations for data quality, reliability, freshness, and issue resolution.
• Support scalable data models, governed metrics, and intuitive self-service analytics experiences.
• Help reduce fragmented reporting and manual reconciliation through shared enterprise data capabilities.
• Cross-Functional Partnership and Adoption Build trusted relationships across Ecommerce, Retail, Merchandising, Planning, Finance, Marketing, and Operations.
• Lead working sessions that connect business priorities to data product decisions and delivery plans.
• Drive adoption through communication, training, documentation, and ongoing stakeholder feedback.
• Measure product impact through usage, user satisfaction, reduced manual effort, and improved decision-making.
• Promote a product-led, customer-focused approach to data across SKIMS.