Staff Analytics Engineer
New
K
Kin InsuranceInsurance
For all other positions, these roles can sit in any of the following 40 states: AL, AR, AZ, CA (exempt only), CO, CT, FL, GA, ID, IL, IN, IA, KS, KY, MA, ME, MD, MI, MN, MO, MT, NC, NE, NJ, NM, NV, NY, OH, OK, OR, PA, SC, SD, TN, TX, UT, VT, VA, WA, and WI. For remote technical positions located in Canada, we are only able to hire individuals who reside in Ontario.Full-TimeStaff
Salary$159K - $187K; $159K – $187K • Offers Equity
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Job Details
- Experience
- 8+
- Required Skills
- Data engineeringData modelingDatabricksLooker
Requirements
- 8+ years in analytics engineering, BI engineering, or data modeling roles.
- Proven track record as a technical anchor on complex, cross-cutting data work.
- Deep expertise in semantic and data modeling (ontology-driven or dimensional).
- Hands-on experience with an ontology or object-based semantic layer.
- Fluency in dimensional modeling for presentation/BI consumption (e.g., Looker/LookML).
- Experience with data mesh, data-as-a-product, and domain-oriented architecture.
- Experience with modern lakehouse platforms (e.g., Databricks) operated as a shared platform.
- Demonstrated technical leadership and influence without formal authority.
- Strong written and verbal communication skills for navigating ambiguity.
- Comfort applying Claude, Claude Code, and Databricks-native AI tools.
Responsibilities
- Own the hardest modeling and architecture in your team's scope—ontology objects and dimensional/semantic models.
- Act as a technical thought partner to product and business leaders to turn ambiguous needs into clear technical plans.
- Take end-to-end ownership of business-critical initiatives requiring deep semantic judgment.
- Align team models with shared core entity representations for consistency and interoperability.
- Define and document modeling patterns, naming conventions, and reference implementations.
- Drive data-as-a-product expectations including contracts, documentation, and reliability.
- Partner with data engineers to shape pipelines feeding clean ontology objects.
- Set patterns for AI-assisted workflows and design semantic layers to be AI-consumable.
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