Analytics Engineer

About This Role

An Analytics Engineer will build and own the company's canonical analytical layer, which serves as a consistent and trusted data foundation for various teams and systems. This role suits a candidate who can combine dimensional modeling and engineering discipline with domain discovery and cross-functional collaboration to establish reliable sources of truth.

Responsibilities

• Design, build, and own canonical data models for the company's core business entities and processes
• Partner with domain experts to translate insurance and operational knowledge into explicit facts, dimensions, grains, relationships, and business rules
• Identify what should be shared across actuarial, reporting, and other consumers while preserving clearly defined domain variants where requirements differ
• Establish the testing, reconciliation, documentation, lineage, ownership, and development practices that make analytical models trustworthy and maintainable
• Evaluate existing models, reports, and actuarial datasets, then lead consumers through migration to reliable, performant shared foundations
• Shape the semantic context that helps people and AI-assisted tools use the company's data correctly, while keeping models coherent as source systems and platform architecture evolve
• Collaborate with the Data Lead and platform engineers to keep analytical models coherent as source systems and platform architecture evolve

Requirements

• Strong SQL skills and hands-on experience developing production data models with dbt
• Deep experience with dimensional modeling, including defining model grain, facts, dimensions, historical state, and relationships between business entities
• Owned important analytical models beyond their initial implementation, including validation, documentation, maintenance, and consumer adoption
• Comfortable learning an unfamiliar domain directly from subject-matter experts and translating incomplete or conflicting requirements into durable technical designs
• Able to lead cross-functional migrations and earn adoption rather than treating model deployment as the end of the work
• Bring sound engineering judgment to analytical systems and value tested, understandable, reusable models over one-off outputs
• Comfortable operating with ambiguity and taking ownership of a problem whose solution has not already been designed

Nice to have:

• Experience in insurance, financial services, or another domain where definitions, historical state, and data quality have material business consequences
• Familiarity with semantic layers, data catalogs, metric definitions, or AI-assisted analytics
• Experience establishing analytics-engineering practices in a growing or relatively early-stage data organization