Analytics Engineer: Build a Trusted Data Core

Shepherd; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Premium Healthcare

Fertility benefits

Unlimited PTO

Daily meals

SF, NYC, Dallas-Fort Worth, Chicago &

Job summary

Shepherd is building a data-driven risk platform and seeks an Analytics Engineer to own the canonical analytical layer. You will design and maintain data models with dbt and SQL, creating shared definitions used across actuarial, reporting, and product teams.

You will lead migrations, enforce data quality, and partner with domain experts to ensure durable, scalable analytics that AI tools can rely on. Reporting to the Data Lead, you will shape the semantic context across the organization.

Qualifications

• Strong SQL skills and production data modeling with dbt.
• Experience with dimensional modeling: grains, facts, dimensions, history.
• Ownership of analytical models: validation, docs, maintenance, adoption.
• Able to learn domain from subject-matter experts and translate requirements.
• Lead cross-functional migrations and drive adoption of models.
• Engineer-friendly mindset: reusable, tested data systems.

Responsibilities

• Design, build, and own canonical data models for core business entities and processes.
• Translate insurance/operational knowledge into facts, dimensions, grains, and rules.
• Collaborate with Actuarial, reporting, and other data consumers for shared foundations.
• Establish testing, documentation, lineage, ownership, and development practices.
• Migrate existing models to reliable, shared foundations and ensure adoption.

Skills

SQL & dbt

Dimensional modeling

Model ownership

Domain discovery

Cross-functional leadership

Engineering judgment

Ambiguity handling

Tools

Python

Dagster

CI/CD

Job description

Shepherd is building a data-driven risk platform and seeks an Analytics Engineer to own the canonical analytical layer. You will design and maintain data models with dbt and SQL, creating shared definitions used across actuarial, reporting, and product teams.

You will lead migrations, enforce data quality, and partner with domain experts to ensure durable, scalable analytics that AI tools can rely on. Reporting to the Data Lead, you will shape the semantic context across the organization.