Data Product Manager

Enable the data products behind Kin’s core insurance decisions, from pricing and underwriting to actuarial analysis and claims. Get to the real question behind each request, then partner with data engineering to build the datasets those teams rely on to decide.

Kin makes life simpler, more affordable, and better for homeowners, especially in the places where climate risks, rising costs, and outdated systems make it harder. We start with smarter homeowners insurance and expand to everything homeowners need to thrive.

Using data, technology, and thoughtful human support, we’re building products that are clear, fair, and help homeowners feel confident — so homeowners aren’t left behind when they need help most.

Founded in 2016, Kin is a remote-first employer with Kinfolk across more than 35 states. We serve customers in 14 states (and counting). Our disciplined growth, strong customer satisfaction, and focus on long‑term sustainability fosters outstanding growth, attracts marquee investors, and earns recognition and accolades, including:

• Built In Chicago's Best Places to Work, Midsize Companies )

• Forbes' America's Best Startup Employers (2026)

• Inc. 5000 Fastest‑Growing Private Companies

• Forbes’ Fintech 50 )

• Great Places to Work Certified (May 2024-May 2027)

Most importantly, we’re building Kin to be a place where people do meaningful work with real impact — for our customers, our communities, and each other. We're excited to tell you more about how you can contribute to our rapid growth, strong unit economics, profitability, and excellent customer ratings. To learn more about how we work and what we’re building, visit  and see how we work.

We’re looking for a data Product Manager to enable the data products behind how Kin prices risk, underwrites policies, runs actuarial analysis, and handles claims. The data that drives those calls lives in many systems, from the claims stack in Snapsheet and Xactimate to vendor loss modeling from RMS and Verisk, alongside our own underwriting and pricing data. This role sits in the middle of that and makes the data usable, so the teams making these decisions can see what is happening and why.

You will enable the data products that drive those decisions rather than owning the calls yourself. That means finding the real question a team is trying to answer and shaping the datasets they need, then partnering with data engineering to build and ship them.

• Dig past the first request to the real decision a pricing, underwriting, actuarial, or claims team is trying to make, asking why enough times to get there

• Enable the data products behind those decisions, from the datasets that feed a rate review to the views a claims team works from

• Connect data across the systems that hold it, including Snapsheet and Xactimate for claims and vendor loss modeling from RMS and Verisk, so a change in any of them is understood and handled

• Build supporting datasets proactively, ahead of the questions, so teams can dig into a metric or a problem without waiting on a new pull

• Scope and deliver data products in partnership with data engineering

• Own the data layer as the single source of truth for core insurance metrics like loss ratio and pricing accuracy, so analysis stays consistent and self serve

• Nail down what each metric means before it ships, since these numbers steer real money decisions on rate and risk selection

• Improve data quality and instrumentation so these decisions rest on numbers people trust

• Translate messy, multi source data into a clear story and a recommendation someone can act on

• Partner across pricing, actuarial, underwriting, claims, and data engineering so insight turns into decisions

In your…