Data Product Lead

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 Lead, 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:

Own the Data Product Life Cycle End to End

• 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.

Gather Requirements and Shape What Gets Built

• 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.

Be the Liaison Between Business, Customers, and Engineering

• 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, present to executives or customers in the afternoon, and write specifications both audiences trust.
• A portfolio of real product artifacts — you have personally authored the working documents of this discipline: product one-pagers and business cases with quantified ROI, PRDs with acceptance criteria, data contract and semantic specifications, metric dictionaries with agreed formulas and owners, adoption and usage dashboards, and sunset/retirement plans. Expect to walk us through examples you wrote.
• Requirements-to-build capability — proven ability to gather, structure, and prioritize business requirements into PRDs and buildable product specifications, validated with consumers at each stage.
• Architecture literacy, not hands-on engineering — you understand modern data platform concepts (cloud warehouse, layered architecture, data contracts, semantic definitions, BI and API serving) well enough to link requirements to our architecture and converse credibly with engineers on Snowflake, dbt, Tableau, and AWS — without building it yourself.
• Cross-functional, cross-geography delivery experience — Agile/Scrum, distributed teams, strong attention to detail, and excellent project management.
• The hiring bar: a data product manager in the full sense of the discipline, already fluent in out business vertical. Someone who owns products from value hypothesis through adoption, usage reporting, and retirement, linkes requirements to our architecture, and can show the artifacts to prove it. Product depth and domain fluency are required; hands-on technology skills are not.

Additional Skills:
Advisory, research, or consulting background experience at an industry research/advisory firm (Gartner-style), a consultancy, or in a client-facing analytics role where explaining data concepts to executives was the job.
Product or data management credentials such as CSPO/PSPO, Pragmatic Institute, SAFe PO/PM, or a data management certification such as CDMP 
AI and GenAI awareness. You have a familiarity with how AI and agentic workflows consume governed data, and comfort using AI-assisted tools in daily work.
Hands-on SQL or BI skills, and experience with data catalogs, semantic layers, or metric stores is helpful, but not required.

Kapitus Total Rewards Package Includes: 

• Competitive Salary Range of $117,800-$189,000 annually. Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant’s final salary will depend on a number of factors including the applicant’s geographic location, skills, and experience
• Annual Incentive Compensation Eligibility - up to 10% annually

Salary Range: 117800.0 - 189000.0