Analytics Engineer

Shepherd • San Francisco, CA

10d • $165,000 - $185,000 • Hybrid

About The Position

Shepherd is a technology-driven Managing General Underwriter (MGU) transforming commercial Property & Casualty insurance for high-hazard industries. Our mission is to make risk frictionless for the builders and operators shaping the physical world, protecting progress from concept through construction and into decades of operation. We're building the fastest, smartest commercial risk platform, where underwriting expertise, data, and automation work together to deliver faster decisions, smarter, more accurate pricing, and better risk outcomes. With Shepherd, safety, speed, and quality no longer trade off against one another. They compound. We're not just modernizing insurance products. We're building the risk infrastructure for the next generation of financial services, where technology, underwriting, and partnerships operate in harmony to support the world's most important industries and the progress they make possible.

As Shepherd grows, more teams, products, and automated systems will depend on data to make decisions. Our challenge is no longer simply getting data into the warehouse. It is turning that data into consistent, trusted representations of Shepherd’s business that can be reused across actuarial, reporting, product workflows, and AI-assisted tools. You will build and own Shepherd’s canonical analytical layer: the shared entities, facts, dimensions, definitions, and business rules that allow those consumers to work from the same foundation. Your work will make it possible for Shepherd to answer important questions consistently, build new analytical products faster, and make AI-assisted analysis more reliable by grounding it in shared business definitions.

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.
• You have owned important analytical models beyond their initial implementation, including validation, documentation, maintenance, and consumer adoption.
• You are comfortable learning an unfamiliar domain directly from subject-matter experts and translating incomplete or conflicting requirements into durable technical designs.
• You can lead cross-functional migrations and earn adoption rather than treating model deployment as the end of the work.
• You bring sound engineering judgment to analytical systems and value tested, understandable, reusable models over one-off outputs.
• You are comfortable operating with ambiguity and taking ownership of a problem whose solution has not already been designed.

Nice To Haves

• 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.
• Familiarity with Python, Dagster, data ingestion pipelines, and modern software-engineering and CI/CD practices.

Responsibilities

• Design, build, and own canonical data models for Shepherd’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 Shepherd’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.

Benefits

• Premium Healthcare: 100% contribution to top-tier health, dental, and vision
• Fertility benefits and family building support
• Unlimited PTO
• Daily lunches, dinners, and snacks
• SF, NYC, Dallas-Fort Worth, Chicago and LA Offices
• Professional Development: Access to premium coaching, including leadership development
• Competitive 401(k) Plan
• Dog-friendly office

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed