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Shepherd is a technology-driven Managing General Underwriter (MGU) transforming commercial Property & Casualty insurance for high-hazard industries. The company raised a $42M Series B in March 2026 (total funding over $60M) and is building the fastest, smartest commercial risk platform where underwriting expertise, data, and automation work together.
As Shepherd's first dedicated Analytics Engineer, you will report directly to the Data Lead and own the company's canonical analytical layer—the shared entities, facts, dimensions, definitions, and business rules that enable consistent decision-making across actuarial, reporting, product, and AI-assisted workflows.
You will design, build, and own canonical data models for Shepherd's core business entities and processes. Working directly with Actuarial, reporting, operations, engineering, and other data consumers, you'll translate insurance and operational knowledge into explicit facts, dimensions, grains, relationships, and business rules. You will identify what should be standardized across teams while preserving clearly defined domain variants where requirements differ.
You will establish the testing, reconciliation, documentation, lineage, ownership, and development practices that make analytical models trustworthy and maintainable. You'll evaluate existing models, reports, and actuarial datasets, then lead consumers through migration to reliable, performant shared foundations. You will 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.
This is a data-modeling and platform-ownership role focused on building trusted foundations, not ad hoc analysis or dashboard building. You'll collaborate with the Data Lead and platform engineers to ensure analytical models remain coherent as the company scales.
REQUIREMENTS:
- Strong SQL skills and hands-on production experience developing data models with dbt
- Deep experience with dimensional modeling, including defining model grain, facts, dimensions, historical state, and relationships between business entities
- Proven track record owning important analytical models beyond initial implementation, including validation, documentation, maintenance, and consumer adoption
- Ability to learn unfamiliar domains directly from subject-matter experts and translate incomplete or conflicting requirements into durable technical designs
- Demonstrated ability to lead cross-functional migrations and earn adoption rather than treating model deployment as the end of work
- Sound engineering judgment applied to analytical systems; preference for tested, understandable, reusable models over one-off outputs
- Comfort operating with ambiguity and taking ownership of problems whose solutions have 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 growing or early-stage data organizations
- Familiarity with Python, Dagster, data ingestion pipelines, and modern software-engineering and CI/CD practices
