Senior Product Manager, Data Products

About Cobalt Streamworks Founded in 2017, Cobalt Streamworks is a comprehensive technology platform powering dispensary operations, while providing consumers with safe and easy access to cannabis. Cobalt Streamworks aims to further support the positive societal change the cannabis industry brings to the world through wellness benefits, social justice, and empowering local communities through tax revenue. Powering thousands of dispensaries across 40+ markets throughout the United States and Canada, Cobalt Streamworks is the leading technology company in the cannabis space and was named in Fast Company’s 10 Most Innovative Companies in North America and listed two years in a row on LinkedIn’s Top 50 Startups. Cobalt Streamworks has raised over $600M in funding to date, backed by D1 Capital Partners, Tiger Global, Dragoneer, DFJ Growth, Thrive Capital, Howard Schultz, Snoop Dogg’s Casa Verde Capital, Gron Ventures, members of the founding team at DoorDash, Kevin Durant’s Thirty Five Ventures, and other notable angel investors. 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 Cobalt Streamworks ’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 Cobalt Streamworks 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 Proven ability to manage complex cross-functional dependencies across multiple engineering teams (data engineering, application engineering, external partners, POS, ecommerce) and ship cohesive outcomes. B2B software experience with an understanding of how platform products are adopted and configured by business operators—not just end consumers Experience leveraging AI/ML technologies (including LLMs) to improve product quality, automate classification, or drive intelligent matching is a strong plus Excellent communication skills with the ability to create clarity across technical and non-technical stakeholders, translating data platform capabilities into business value Experience designing governance models for shared data assets—resolving conflicts between contributors, managing data provenance, and balancing canonical authority with contributor flexibility Comfort owning product identifier systems (SKU hierarchies, parent-child product relationships, cross-jurisdiction variants) as a product problem, not just an engineering one It’s a Bonus if You… Experience in cannabis, regulated industries, or verticals with complex compliance-driven product categorization requirements Familiarity with modern data stack tooling (dbt, Dagster, Snowflake, or similar orchestration and transformation tools) Background in building or scaling B2B marketplace pla