Staff Analytics Engineer

Join Kin, a fast-growing insurtech company, as a Staff Analytics Engineer. In this role, you will be the technical anchor for an analytics engineering team, owning ontology design, semantic modeling, and the patterns your team builds on. You will work within the Data Engineering organization to turn raw, domain-owned data into a shared, trusted semantic model of the business. You will also act as a technical thought partner to product and business leaders, translating ambiguous needs into clear, durable technical plans.

• Assumer la responsabilité de la conception de l'ontologie, de la modélisation sémantique et des modèles de données de votre équipe.
• Agir en tant que partenaire technique pour les leaders produits et commerciaux, en traduisant les besoins ambigus en plans techniques clairs.
• Prendre en charge les initiatives les plus critiques pour l'entreprise, en utilisant votre jugement sémantique et architectural.
• 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
• Experience with modern lakehouse platforms (e.g., Databricks) operated as a shared, self-serve data platform
• Fluency in dimensional modeling for presentation/BI consumption (e.g., Looker/LookML) downstream of a source-of-truth model
• Comfort applying Claude, Claude Code, and Databricks-native AI tools in day-to-day analytics engineering work
• 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
• Experience with data mesh, data-as-a-product, and domain-oriented architecture — or strong, well-reasoned conviction about how federated data ownership should work
• 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
• 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
• Python, Git-based workflows, or transformation frameworks such as SQLMesh or dbt
• Experience with Foundry Pipeline Builder/Functions or performance tuning at scale