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impact.com is seeking a Technical Product Manager to own the foundational data layer powering benchmarking, classification, and personalization systems. You will manage taxonomy ecosystems spanning partner, brand, product, and content classification, with responsibility for data quality and production readiness.
In this hands-on role you will work inside the dbt/pull-request workflow with engineering, apply data governance, and translate taxonomy insights into concrete recommendations for
impact.com is the world's leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products- Performance (affiliate), Creator (influencer), and Advocate (customer referral)-unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L'Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.
We're seeking a Technical Product Manager to own the foundational data layer that powers our benchmarking, recommendation, classification, and personalization systems. In this role, you'll own the operational and structural integrity of our taxonomy ecosystem - spanning partner/brand/product/content classification, proprietary vertical taxonomies, and the pipeline discipline that gets new taxonomies safely into production. This is a role that demands precision, technical fluency, and comfort with ambiguity in messy real-world data - not roadmap ownership for external-facing products. You'll spend your time in classification logic, data quality audits, and the mechanics of promoting data assets from prototype (BigQuery) to production (SingleStore), working directly inside the dbt-and-pull-request workflow alongside engineering. Your work will directly influence the accuracy, consistency, and trustworthiness of the data foundation that every downstream product (search, scoring, recommendations, reporting) depends on.