Staff Analytics Engineer

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simpler, more affordable, and better for homeowners — especially in the places where climate risks, rising costs, and outdated systems make it harder.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

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. Understanding the business is part of the craft here, not someone else's job.

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 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

In your first 6–12 months at Kin, success is less about checking boxes and more about the impact you create. You’ll use your skills and judgment to take ownership of meaningful work, improve how we operate, and help move Kin’s mission forward. Along the way, you’ll deliver outcomes that make a real difference for both Kinfolk and the homeowners we serve.

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.

The hardest modeling problems in your team's scope are solved durably — your team's ontology objects and presentation-layer models are stable, documented, and trusted by the business partners who depend on them

Product and business partners bring you in early on their hardest problems and trust the technical direction you set

Your team builds on the patterns you've established without needing your review on routine work, measurably reducing bottlenecks

A previously intractable modeling problem in your team's domain is solved and documented as a reference others across the mesh can follow

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 — and 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 the 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 — or strong, well-reasoned conviction about how federated data ownership should work

Experience with modern lakehouse platforms (e.g., Databricks) operated as a shared, self-serve data platform

Demonstrated technical leadership and influence without formal authority — you move a team and its partners through credibility, clarity, and example

Strong written and verbal communication, especially when navigating ambiguity, tradeoffs, or disagreement

Comfort applying Claude, Claude Code, and Databricks-native AI tools in day-to-day analytics engineering work

Bonus if you have:

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 hire

We believe a great hiring experience should be clear, respectful, and human. We’ll accept applications for this position until September 11, 2026. While our recruiting team uses AI tools for efficiency, resumes are still screened by Kin’s in-house recruiters, and candidate evaluations and hiring decisions are made by recruiters and hiring teams. Rest assured, real people make real decisions.

The hiring process and timeline for each role will vary, depending on the position. However, here are some things you can expect from us:

Prompt updates and feedback following interviews

Interviews with recruiters, hiring managers, and members of teams

Skills assessment relevant to the position, if applicable

Genuine, thoughtful human interaction at every step

How we support you

We offer a comprehensive, competitive benefits program, allowing you