Analytics Engineer

What We Do

Yesterday's insurance wasn't built for today's risk. We see it in the data and we feel it in the field. Emerging technology can reinvent how risk is priced and managed, faster and smarter, anchored in proven expertise. First-movers will define the next era of commercial risk management, and Shepherd is building it. 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
• 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.

Our Investors

In March 2026, Shepherd raised a $42M Series B — bringing total funding to over $60M — led by Intact Private Capital, the investment arm of one of the largest insurers in the world. Intact is not only our lead investor but also a carrier partner, a testament to the confidence the incumbent industry has in what we're building. Our investors:

• Intact Private Capital (led our Series B round)
• Costanoa Ventures (led our Series A round)
• Spark Capital (led our Seed round)
• Susa Ventures (lead our Pre-Seed round)
• Y Combinator
• And several others

Our Team

We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our About page to learn more.

Analytics Engineer

The Mission: Build Shepherd’s Trusted Data Foundation

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.

Responsibilities & Expectations

• Develop production-quality dimensional models using dbt including defining model grain,

facts, dimensions, historical state, and relationships.

• Own analytical models end-to-end including validation,

documentation, maintenance, and consumer adoption.

• Collaborate cross-functionally with actuarial teams,

data operations, and engineering teams.

• Establish analytics-engineering practices including testing,

reconciliation, documentation, data lineage, and ownership.

• Support migrations from legacy systems or one-off outputs towards standardized models.
• Work closely with Data Lead on prioritization of analytics projects.
• Contribute to continuous improvement of data infrastructure.

Qualifications & Skills Needed

Experience & Background

• Strong SQL skills required.
• Hands-on experience developing production data models using dbt preferred.
• Familiarity with Python nice-to-have.

Technical Skills & Tools

• Familiarity with Dagster or other orchestration tools nice-to-have.
• Experience working with data ingestion pipelines nice-to-have.
• Knowledge of semantic layers or data catalogs nice-to-have.

Methodologies & Practices

• Dimensional modeling expertise including grain definition and historical state management.
• Strong analytical model ownership discipline including testing and documentation.

Soft Skills & Collaboration

• Ability to learn unfamiliar domains quickly from subject-matter experts.
• Translate incomplete or conflicting requirements into durable technical designs.
• Lead cross-functional migrations earning adoption across teams.
• Sound engineering judgment valuing tested reusable models over one-off outputs.

Domain Expertise & Industry Knowledge

• Insurance industry experience nice-to-have but not required.

Other Skills & Attributes

• Comfortable operating with ambiguity taking ownership of unsolved problems.
• Urgency initiative mindset ('go get it').

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