Senior Product Manager, Data Products

About This Job

As the Senior Product Manager for Data Products, you will own the vision, strategy, and roadmap for key platform products that leverage Dutchie’s massive data assets to solve high-impact business problems. You will focus on critical data domains, ranging from product identity and taxonomy to advanced AI-driven systems. This includes:

• Developing and governing the core Product Catalog and data platform.
• Enhancing recommendations systems for e-commerce.
• Optimizing Ads optimization systems.
• Pioneering new data-intensive initiatives that are foundational to the Dutchie platform.

This is a high-impact, deeply cross-functional role. Your products will serve internal teams and external customers, ensuring that a clean, consistent data layer compounds value across all surfaces, workflows, and analytics that span retailers.

What You'll Do...

• Own the end-to-end product vision, strategy, and roadmap for a portfolio of data products consumed across ecommerce, POS, B2B, and analytics.
• Partner closely with Data Engineering to evolve the underlying data platform—including matching, classification, canonical naming, and quality—to deliver measurable customer and business outcomes.
• Define and ship data-driven experiences for our customer bases: retailer-facing (e.g., operational efficiency), business-facing (e.g., content contribution via partner portals), and internal (e.g., APIs, SLAs).
• Develop next-generation AI and Machine Learning systems, such as next best action and recommendations engines, to drive significant product improvements.
• Own the architecture and governance models for critical data assets (e.g., product identifiers, taxonomies), resolving conflicts and balancing canonical authority with contributor flexibility.
• Translate data coverage, quality, and structure into measurable business outcomes, such as merchandising conversion, receiving efficiency, or analytic integrity.

What You Bring...

• 7+ years of progressive experience in product management, with demonstrated ownership of working with data products and production-grade machine learning systems.
• Experience turning internal data infrastructure into externally-facing products or platform capabilities—not just maintaining pipelines, but defining how data becomes a product and customer experiences.
• Strong technical fluency across data pipelines, ML/NLP systems, and data quality tooling; comfort engaging deeply with data engineering teams on architecture, pipeline design, and quality metrics.
• Experience with product catalog, taxonomy, or product information management (PIM) systems, ideally in a multi-sided marketplace or platform context.