Data Product Manager

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Kapitus is building a next-generation enterprise data and AI capability that replaces hundreds of legacy analytics workflows with governed, reusable business data products — moving the organization from producing reports to producing decisions. As a Data Product Manager, you own data products through their full life cycle — from business problem and value hypothesis, through business case, build, launch, adoption, and usage reporting, to retirement of products and the legacy reports they replace. You are the bridge between business requirements, data engineering, governance, and customer-facing teams, and you manage products against outcomes business leaders recognize, not technical output. This is a product-discipline role, not a hands-on engineering role. You must understand our architecture well enough to link requirements to it and hold credible conversations with engineers. Experience in our business vertical is required, not preferred: you will not be learning the language of lending on the job. AI fluency is an advantage, not a requirement. What You Will Do:

Identify business problems worth solving with data; clarify each product's value proposition — utility, decision enabler, or driver of new business value — and quantify expected impact before anything is built. Define outcome-driven metrics that link each product to business goals (revenue growth, cost, risk, customer experience), alongside standardized product KPIs — adoption, usage, quality, service levels, cost-to-serve — maintained in a metrics dictionary with agreed formulas and owners; avoid vanity metrics that measure activity instead of value. Own usage reporting and portfolio transparency: scorecards, value stories co-created with stakeholders, and regular reviews informing invest, pivot, or retire decisions. Own retirement: sunset policies, end-of-life roadmaps, and structured decommissioning for products — and legacy workflows — that fail to sustain business value. Elicit requirements through stakeholder interviews, focus groups, and customer journey mapping; translate vague, ill-defined needs into well-defined, testable product requirements with clear acceptance criteria. Turn requirements into product definitions engineering can build: named owners, defined consumers, data contracts, certified semantic definitions, quality thresholds, and service levels. Link every requirement to our platform architecture — layered data foundation, governed core entities, certified semantics, and serving channels (BI, APIs, ML, AI) — so products are built on the platform, not beside it. Champion prototyping and iteration: confirm value with consumers at every development stage rather than specifying once and walking away. Serve as the connective tissue between business requirements, data engineering, governance, and customer-facing teams, translating in both directions so needs and delivery stay aligned. Engage business and technical stakeholders to communicate progress, surface trade-offs, and present recommendations clearly to technical, non-technical, and customer audiences. Serve as the primary coordination point between U.S. and India-based teams, ensuring alignment on priorities, requirements, handoffs, and delivery timelines. Partner with Data Engineering on backlog priorities and sequencing, and with Data Governance on definitions, standards, quality, lineage, and appropriate usage — in an Agile/Scrum environment.

What We’re Looking For:

5+ years in data product management, product ownership, or customer-facing data & analytics roles in a data-intensive environment. Bachelor's degree in Business, Analytics, Computer Science, Information Systems, or a related field, or equivalent practical experience. Business vertical experience — required. 3+ of those years working with data in small business lending, commercial or consumer credit, merchant financing, payments, or closely adjacent financial services. You already speak the language of the business — originations and funnel conversion, underwriting and credit policy, pricing and risk tiers, funding, servicing, delinquency and collections, renewals, and portfolio performance — and can engage business partners as a peer from day one. We will not be teaching the vertical. Command of the data product management discipline — the difference between running a product and running a project; value propositions and value hypotheses; business cases; stage-gated life cycle management from ideation through build, launch, operation, scaling, and retirement. Outcome orientation — you measure products by business outcomes, not technical output: defining outcome-driven metrics with stakeholders, tracking product KPIs (adoption, usage, quality, service levels, cost-to-serve), producing usage reporting and portfolio scorecards, and telling credible value stories. Excellent communication and stakeholder management — the defining skill of this role. You can run a requirements workshop in the morning, p