Staff Analytics Engineer

Role Description

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.

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.

Your responsibilities include:

• 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.
• 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.

Qualifications

• 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.

Requirements

• 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.

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.