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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** BUILD, Inc. - **Location:** United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Computing Platforms, Continuous Integration, Database Queries, Dimensional Modeling, Python (Programming Language), Metadata Repositories, Operational Databases, Data Layers, Data Analytics, Data Management, Data Pipelines - **Published:** October 8, 2026 - **Apply:** https://startup.jobs/analytics-engineer-shepherd-10334998 ## About the Role * 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., * 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. ## Description 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. The Role You will be Shepherd's first dedicated Analytics Engineer and will report directly to the Data Lead. This is a high-ownership role for someone who can combine dimensional modeling and engineering discipline with the domain discovery and cross-functional collaboration required to establish trusted sources of truth. You will work directly with Actuarial, reporting, operations, engineering, and other data consumers to understand how important business concepts are represented today. You will identify what should be standardized, where legitimate domain differences must remain, and how those decisions should be implemented, tested, documented, and adopted. Building on an existing warehouse, dbt project, and body of analytical work to establish Shepherd's canonical analytical layer. While working with domain experts across the company, you'll determine which business concepts to model first and lead their design, implementation, and adoption. This is a data-modeling and platform-ownership role, not an ad hoc analysis or dashboard building position. What You'll Do * 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.