Staff Analytics Engineer

About the role

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simpler, more affordable, and better for homeowners - especially in the places where climate risks, rising costs, and outdated systems make it harder.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 (2021-2026) Forbes' America's Best Startup Employers (2026) Inc. 5000 Fastest-Growing Private Companies Forbes' Fintech 50 (2023-2026) 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 kin.com and see how we work . 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. Understanding the business is part of the craft here, not someone else's job.

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 the product and business leaders your team supports: understand their goals deeply and 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 - partnering with the Principal Engineer and peers where definitions are cross-cutting
• Define the modeling patterns, naming conventions, and reference implementations your team builds on, and contribute them back to the discipline's shared standards
• Drive data-as-a-product expectations within your team's scope - ownership, contracts, documentation, and reliability for what your team owns
• Partner with domain data engineers to shape the data contracts and pipelines that feed clean, well-defined ontology objects, and surface upstream issues that degrade your team's models
• Raise the technical bar through model and design review, pairing, mentorship, and contributions to hiring and onboarding
• Set your team's patterns for applying Claude and Claude Code to analytics engineering work, and design the ontology and semantic layer to be AI-consumable so tools like Databricks Genie can reason over your team's data reliably
• Success in this role
• By the end of your first year, you should feel confident in your role, trusted as an owner, and proud of the progress you've helped make.
• Th

Benefits

Additional Information

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.

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at kin? Share your experience