Staff Analytics Engineer

Kin Insurance

1d•$159,000 - $187,000•Remote

About The Position

We're looking for a Staff Analytics Engineer to be the technical anchor of one of Kin's analytics engineering teams. This role involves owning the ontology design, semantic modeling, and the patterns your team builds upon. Within the Data Engineering organization, Analytics Engineering transforms raw, domain-owned data into a shared, trusted semantic model of the business. As Kin adopts a data mesh and an ontology-driven source of truth, each analytics engineering team will own a significant part of this model. You will be responsible for tackling the most complex modeling and design challenges within your team's scope, from ontology objects representing your business slice to the dimensional and semantic models used downstream in BI and self-service. You will also serve as a technical thought partner to product and business leaders, translating ambiguous needs into clear, durable technical plans. Understanding the business is an integral part of this role.

Requirements

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

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

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

Benefits

Competitive salary and company equity through Restricted Stock Units (RSUs), granted as part of our standard compensation package and based on role and level
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
A variety of supplemental benefit options, 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 employees take 15–20 days per year), plus 8 company-observed holidays
Paid parental leave, including 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 across the organization
Professional development budgets for certifications, conferences, and learning available, subject to management approval