Staff Analytics Engineer - Kin

Quick Summary

You're the technical anchor for an analytics engineering team—owning ontology design, semantic modeling, and the patterns your team builds on. 8+ years required.

Who We Are

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simpler, more affordable, and better for homeowners—especially in the places where climate risks, rising costs, and outdated systems make it harder.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).

The Opportunity

We're looking for a Staff Analytics Engineer to be the technical anchor of one of Kin's analytics engineering teams—the person who makes your team's slice of our shared data model correct, durable, and trusted.

Within the Data Engineering organization, Analytics Engineering turns raw, domain-owned data into a shared, trusted semantic model of the business. As Kin moves to a data mesh—where domain teams own their data as products on a shared, self-serve platform—and adopts an ontology-driven source of truth, each analytics engineering team owns a meaningful piece of that model. You'll own the hardest modeling and design problems in your team's scope, from the ontology objects that represent your slice of the business to the dimensional and semantic models that serve them downstream in BI and self-service. You'll also be a technical thought partner to the product and business leaders your team supports—going deep enough on their goals to turn ambiguous needs into clear, durable technical plans.

Your Responsibilities

• Own the hardest modeling and architecture in your team's scope—ontology objects (types, properties, link types, and actions) that model your part of the business as it actually operates, and the dimensional and semantic models (e.g., Looker/LookML) that serve them downstream
• Act as a technical thought partner to product and business leaders: translate ambiguous or conflicting business needs into clear, durable technical plans
• Take end-to-end ownership of your team's most business-critical initiatives, where deep semantic and architectural judgment is the differentiator
• Align your team's models with shared representations of core entities (customer, policy, claim) so they stay consistent and interoperable across the mesh
• Define modeling patterns, naming conventions, and reference implementations your team builds on, contributing them back to shared standards
• Drive data-as-a-product expectations within your scope—ownership, contracts, documentation, and reliability
• Partner with domain data engineers to shape data contracts and pipelines that feed clean ontology objects
• Raise the technical bar through model and design reviews, pairing, mentorship, and contributions to hiring and onboarding
• Set patterns for applying Claude and Claude Code to analytics engineering work, and design the ontology and semantic layer to be AI-consumable (e.g., for Databricks Genie)

What You’ll Bring

• 8+ years in analytics engineering, BI engineering, or data modeling roles, with a track record of being the technical anchor on complex, cross-cutting data work
• Deep expertise in semantic and data modeling—with the judgment to know when an ontology-driven model, a dimensional model, or both is the right tool
• Hands-on experience with an ontology or object-based semantic layer (e.g., Palantir Foundry Ontology), or strong transferable modeling experience and appetite to go deep
• Fluency in dimensional modeling for presentation/BI consumption (e.g., Looker/LookML) downstream of a source-of-truth model
• Experience with data mesh, data-as-a-product, and domain-oriented architecture
• Experience with modern lakehouse platforms (e.g., Databricks) operated as a shared, self-serve data platform
• Demonstrated technical leadership and influence without formal authority
• Strong written and verbal communication skills when navigating ambiguity or tradeoffs
• Comfort applying Claude, Claude Code, and Databricks-native AI tools in day-to-day analytics engineering work

Nice-to-Haves:

• Python, Git-based workflows, or transformation frameworks such as SQLMesh or dbt
• Experience with Foundry Pipeline Builder/Functions or performance tuning at scale

How We Support You

• Competitive salary and company equity through Restricted Stock Units (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: 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; 8 weeks at 100% pay for non-birthing parents)
• Professional development budgets for certifications, conferences, and learning

Location Eligibility

This position can be performed remotely from any of the following 41 eligible US states: AL, AR, AZ, CA (exempt only), CO, CT, FL, GA, ID, IL, IN, IA, KS, KY, LA, MA, ME, MD, MI, MN, MO, MT, NC, NE, NJ, NM, NV, NY, OH, OK, OR, PA, SC, SD, TN, TX, UT, VT, VA, WA, and WI.