Data Product Manager Jobs in Wisconsin (NOW HIRING)

At Cotality, we are driven by a single mission-to make the property industry faster, smarter, and more people-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.
Job Description:
Role Summary
The Principal Technical Product Manager for AI Integration owns the strategy and delivery of Cotality Insurance's external-facing AI integration layer. This includes the MCP server ecosystem, AI/API gateway, developer experience, and the platform that enables AI agents from clients and third-party partners to discover, authenticate, and consume Cotality's insurance data products autonomously.
This is a technical product leadership role with a player-coach model. You set the product direction, make architecture tradeoff decisions alongside Architecture, and drive execution through a dedicated engineering team and cross-functional application team partners who own the underlying product APIs.
Core Responsibilities
MCP Platform Strategy & Roadmap. Define which Cotality data products get exposed as MCP tools, in what order, and with what capabilities. Align the connector roadmap with business priorities across Claims, Underwriting, Catastrophe Risk, and Contractor Solutions. Own the sequencing decisions - what ships this quarter, what's next, and why. Target cadence: one new MCP connector live per month.
Tool Schema & Data Product Definition. Partner with product teams and the Data Architect to define what data gets exposed through each MCP tool - the fields, the boundaries, the descriptions that AI agents read to decide when and how to use a tool. This is the highest-leverage work in the role. The quality of tool schemas directly determines whether an AI agent can use Cotality's data effectively or makes errors that damage client trust.
Architecture & Technical Direction. Work with the Architect and engineering team to define gateway configurations, authentication flows, and the aggregation layer. Make tradeoff decisions - when to optimize for speed vs. extensibility, when to wrap an existing API vs. build a composite tool, when to ship and iterate vs. get it right the first time. You don't write the code, but you understand the architecture deeply enough to lead technical decisions.
Developer Experience. Own the end-to-end experience for external developers and AI platforms integrating with Cotality's MCP endpoints. This includes the developer portal, API documentation, sandbox environments, authentication guides, SDK examples, and onboarding workflows. You understand what good developer experience feels like because you've been the developer - you've integrated against third-party APIs, read bad documentation, and know the difference between a portal that accelerates adoption and one that generates support tickets. Target: a developer or AI agent goes from zero to working Cotality data in under 60 minutes.
Data Provenance & Trust. Own the strategy for ensuring data delivered through MCP tools is verifiable, auditable, and resistant to misattribution or hallucination by consuming AI agents. Define response metadata standards, logging requirements, and the guardrails that protect Cotality's brand when data flows through systems Cotality doesn't control. This includes near-term controls (response signing, audit logs, server instructions) and the longer-term innovation roadmap for data provenance.
Go-to-Market Coordination. Work with product marketing, sales engineering, and business development to position the MCP platform for carrier clients and AI platform partners. Support demos, pilot programs, and partner integrations.
Cross-Functional Execution. Drive delivery through a dedicated MCP engineering team (MCP Architect, MCP Engineers, Data Architect) and application team partners who own the underlying product APIs. You define what needs to be built, set priorities, and shape tool designs. Application teams contribute significant implementation effort, particularly around exposing their product APIs as MCP-ready services. Keeping multiple workstreams aligned and moving toward shared delivery timelines is a core part of the role.
Job Qualifications:
Required

• 8+ years in technical product management or architecture, with at least 3 years owning API platforms, developer tools, data products, or integration infrastructure
• Engineering background - you've written code professionally and carry an intuitive understanding of what makes a great developer experience from the consumer side, not just the provider side
• Demonstrated experience shipping developer-facing or machine-consumable products with measurable adoption metrics
• Strong understanding of API gateway patterns, OAuth/authentication flows, and how distributed systems communicate
• Working knowledge of how large language models consume tools - function calling, tool descriptions, context windows, and the failure modes that arise when AI agents interact with external data sources
• Experience defining data products for external consumption - what to expose, what to withhold, how to structure responses for different consumer types, and how to manage data quality and trust
• Track record of driving delivery through cross-functional teams without direct reporting authority - influencing engineering, product, and business stakeholders to execute against a shared roadmap
• Exceptional written communication - you will write tool descriptions that AI agents read, developer documentation that humans read, strategy documents that leadership reads, and contract language that legal reviews
• Comfort with ambiguity and speed - this is a new category with no playbook, requiring decisions with incomplete information, shipping MVPs, and iterating rapidly

Preferred

• Experience with specific API gateway technologies (Apigee, Kong, AWS API Gateway, or equivalent)
• Hands-on experience with developer documentation and portal platforms - Mintlify, Swagger/OpenAPI, Redoc, ReadMe, or similar - and a strong opinion on what makes API documentation actually usable
• Familiarity with the Model Context Protocol (MCP) or equivalent agent-tool interface standards
• Experience in insurance, financial services, or regulated data industries
• Understanding of data provenance, audit requirements, and compliance considerations in regulated environments
• Experience with pricing and packaging of API/data products (per-query, tiered access, usage-based models)
• Background in or exposure to AI/ML workflows, particularly how enterprises are deploying AI agents in production

What Differentiates the Ideal Candidate
This role is not about managing a backlog and writing user stories. The ideal candidate is someone who thinks about how machines consume data - not just how humans use software. They understand that a tool description is an instruction set for an LLM, that a missing field in an API response can trigger hallucination in an agent, and that the developer experience for an AI platform partner looks fundamentally different from the developer experience for a human integration engineer.
They are comfortable being the bridge between deeply technical architecture discussions and business strategy conversations. They can explain to an engineer why a tool schema needs to be restructured for agent effectiveness, and they can explain to a VP of Sales why a carrier's AI agent using Cotality data is a stickier revenue relationship than a traditional API integration.
They see the opportunity: agent-consumable insurance intelligence is a new product category, and the person in this role gets to define it.
Annual Pay Range:
111,900 - 175,000 USD
Application Window:
This opportunity is expected to remain posted through the date identified below, subject to business needs.
Thrive with Cotality
At Cotality, we offer more than just a job, we provide a benefits experience designed to support your whole self. From a flexible working model to competitive time off and standout health coverage with meaningful perks and growth opportunities, our package is built to help you thrive at work and in life.
Highlights, depending on role classification, include:

• Time off: Generous PTO and 11 paid holidays, plus well-being and volunteer time off.
• Family Support: Up to 16 weeks of fully paid parental leave and a baby stipend.
• Health: Multiple medical plan options with mental health and wellness support offerings.
• Retirement: 401(k) with company match and vesting after one year.
• Financial Perks: $400 annual well-being stipend and tuition assistance up to $5,250.
• Extras: Recognition Rewards, Referral bonuses, exclusive discounts and more!

Cotality is an Equal Opportunity employer committed to attracting and retaining the best-qualified people available, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, disability or status as a veteran of the Armed Forces, or any other basis protected by federal, state or local law. Cotality maintains a Drug-Free Workplace.
Cotality is fully committed to a work environment that embraces everyone's unique contributions, experiences and values. We offer an empowered work environment that encourages creativity, initiative and professional growth and provides a competitive salary and benefits package. We are better together when we support and recognize our differences.
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