Product Manager

\n Why This Role Exists
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Profisee is how enterprises finally solve the data quality issues that have been holding back their most strategic initiatives. Our AI-first master data management (MDM) platform cleans, unifies, and improves critical enterprise data, providing the trusted foundation organizations need for AI, analytics, and digital transformation.
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AI systems fail in enterprises not because the models are weak but because the data underneath them is ambiguous, duplicated, and undocumented. An agent that retrieves three versions of the same customer will answer confidently and wrongly. This role exists to close that gap through data governance, data catalog integration, data products, and third-party data enrichment — resolved entities, meaningful semantics, explicit ownership, and enforceable access policy, delivered in a form an agent can actually consume.
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\n Reporting & Key Relationships
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You will work closely with engineering, design, pre-sales, customer success, marketing, catalog platform partners (Microsoft Purview, Databricks Unity Catalog, Collibra, Alation, Atlan), and external data providers (e.g., Melissa, Loqate, Dun & Bradstreet, Experian, ZoomInfo). You are expected to become the go-to authority for this product domain — internally and with customers — within your first year.
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\n Key Performance Objectives
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1. Complete structured discovery and publish a prioritized roadmap across all four domains.
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By end of Month 3
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Interview a representative set of customers, data stewards, and CDO-organization stakeholders, and mine support escalations and pre-sales/CS feedback, across data governance, catalog integration, data products, and enrichment. Translate that research into a prioritized, written set of problems and opportunities that engineering leadership and go-to-market stakeholders sign off on as the basis for the next two quarters of work.
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2. Define and ship an initial, buildable version of the data product concept.
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By Month 6
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Land a clear definition of a Profisee data product — a curated, owned, versioned set of mastered data with a documented contract, quality guarantees, an access policy, and a consumption endpoint (delivered primarily through our MCP server). Specify it precisely enough that engineering can build against it without re-litigating the concept, and clearly enough that sales can position and demo it to at least one design partner or active prospect.
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3. Deliver a catalog integration that customers recognize as a genuine step up from a one-way export.
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By Month 9
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Ship bidirectional integration with at least one major catalog platform (e.g., Microsoft Purview, Collibra) covering both outbound publishing (entity models, quality scores, lineage, stewardship) and inbound inheritance (glossary terms, classifications, sensitivity labels). Validate success with direct confirmation from a reference customer who already has meaningful investment in that catalog.
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4. Bring at least one new enrichment capability to market with pricing and margin resolved.
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By Month 12
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Evaluate the third-party enrichment provider landscape (address verification, firmographic/contact enrichment, industry reference data) well enough to make build/buy/partner calls with conviction. Launch at least one new enrichment capability with metering, packaging, and pricing fully resolved — not deferred — and hit an agreed gross margin target after pass-through provider costs.
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5. Turn provider onboarding into a repeatable product motion.
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By Month 12
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Build the certification, credentialing, and integration framework needed so that onboarding the second and third enrichment providers takes measurably less time and engineering effort than the first — evidence that onboarding has become a product capability rather than a one-off project each time.
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6. Establish yourself as the credible internal and external authority in this domain and connect that authority to business outcomes.
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Ongoing, assessed at 12 months
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Become the person engineering, sales, pre-sales, and marketing turn to first on governance, catalog, data product, and enrichment questions. Participate directly in pre-sales engagements that use governance and data-product capabilities to reach the Chief Data Officer buying center, and show a measurable connection between that work and deal size, competitive win rate, or shortened evaluation-to-production timelines.
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\n Competencies
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• A track record of owning a product area end-to-end — from discovery and strategy through delivery and iteration — in a B2B SaaS or enterprise software environment (typically 3+ years of PM experience, though the pattern of ownership matters more than the tenure).
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• Real, demonstrable understanding of data governance in practice: how data organizations actually use catalogs, how metadata is maintained and consumed downstream, and why governance programs succeed or fail — as a product owner or as a practitioner who ran one of these programs.
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• A defensible, articulated point of view on how governed data and metadata feed AI systems — specifically why entity resolution and semantic context determine whether an AI answer can be trusted.
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• Experience shipping integrations or capabilities through agile engineering teams: sprint planning, backlog prioritization, and writing specifications engineers can build against without hand-holding.
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• Comfort with the commercial mechanics of product work — pricing, packaging, metering, and vendor/provider cost structures — since two of the six objectives above are explicitly commercial.
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• A history of running customer research (interviews, escalation analysis, pre-sales/CS feedback loops) and turning it into decisions, not just documentation.
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• Communication that works in both directions — able to hold their own with engineers on a technical integration question and with senior leadership on why it matters to the business.
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• A demonstrated tolerance for ambiguity: someone who has taken a vaguely-scoped, cross-functional problem and driven it to a resolved, shippable outcome.
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\n Additional Background That Strengthens Your Candidacy
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• Direct experience at or with a data catalog or metadata platform — Microsoft Purview, Collibra, Alation, Atlan, Databricks Unity Catalog, Snowflake Horizon, or Informatica.
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• Familiarity with data mesh, data product, and data contract practice, including a grounded view of where these ideas hold up in large enterprises and where they don't.
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• Experience grounding AI or agent systems in enterprise data through retrieval, MCP, or comparable approaches.
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• Experience with master data management or data integration platforms.
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• Direct experience with third-party data providers such as Melissa, Loqate, Dun & Bradstreet, Experian, or ZoomInfo — as a buyer, integrator, or product owner.
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• Experience owning an API-metered or consumption-priced product capability.
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• Familiarity with regulatory and control frameworks that drive governance programs — GDPR, CCPA, HIPAA, or frameworks like BCBS 239.
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• Familiarity with the Microsoft ecosystem — Azure, Microsoft Fabric, Power BI, or Dynamics.
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• Working technical knowledge of databases, SQL, APIs, and cloud architecture.
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• Prior experience directly supporting pre-sales or customer-facing engagements as part of a product role.
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• Experience working with global partner ecosystems, including systems integrators and value-added resellers.
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\n How Performance Will Be Reviewed
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We evaluate performance across four competencies: User-Centric Mindset, Communication and Collaboration, Execution, and Business Outcomes. We invest in the growth and development of our team members and believe in providing honest, constructive feedback.
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We value strong cross-functional relationships and believe the best products come from teams where engineering, design, product, and go-to-market functions work closely together.
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Our product team operates with a high degree of ownership. Product Managers are not project managers or ticket writers. You are expected to be a domain expert, a customer advocate, and a strategic thinker who can also roll up your sleeves and get things done.