Data Product Manager

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.

Responsibilities

Dig past the first request to the real decision a pricing, underwriting, actuarial, or claims team is trying to make; 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; Build supporting datasets proactively, ahead of the questions; 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; Nail down what each metric means before it ships; Improve data quality and instrumentation; 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

Requirements

2 to 5 years in product management, data analytics, or an analytical role close to the insurance business; Real business sense for how data gets used to decide, ideally from time spent as an analyst making recommendations, not only shipping features; Advanced SQL, able to pull and join data across multiple sources on your own; Comfort in a modern data stack such as Databricks and Looker; The instinct to dig for the real question instead of taking the first request at face value; Precise about definitions, since a metric that drives a pricing or claims decision has to mean one thing; Able to use AI to raise your own throughput and prototype ideas fast; Experience partnering with engineering to scope and ship; Some familiarity with the insurance business, or a clear appetite to learn it fast; Clear, concise communication with a technical audience and a business one alike

Nice to have

Background in InsurTech or FinTech, especially homeowners, and comfort with concepts like loss ratio, premium, underwriting, and catastrophe risk; Familiarity with insurance systems such as Snapsheet, Xactimate, or catastrophe modeling from RMS and Verisk

Benefits

Competitive salary and company equity through RSUs; 401(k) with company match up to 4% of eligible earnings; Multiple medical plan options, plus dental and vision coverage; Company-funded HSA contributions (based on medical plan selection); Company-paid life insurance and short-term disability; Supplemental benefits including long-term disability, critical illness, accident, legal, and pet insurance; Access to mental health support and confidential counseling resources; Flexible PTO for exempt employees (most take 15–20 days per year) plus 8 company-observed holidays; Paid parental leave, up to 14 weeks at 100% pay for birthing parents and 8 weeks at 100% pay for non-birthing parents; Career mobility and internal growth opportunities; Professional development budgets for certifications, conferences, and learning, subject to management approval