Analytics Engineer
Confirmed on the employer's own hiring board on Oct 7, 2026. First seen by Alion on Oct 7, 2026. Shepherd scores C on the Alion truth index.
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
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.
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.
Who You Are
Required
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 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 a growing or relatively early-stage data organization.
Familiarity with Python, Dagster, data ingestion pipelines, and modern software-engineering and CI/CD practices.
How we work
Shepherd runs on four values. Here's what each one means in this seat.
Think big, build big. We exist to protect progress and the industries that rely on it. The work here is aimed at a system that runs on its own, and the roadmap gets sequenced backward from that rather than forward from what's easy.
Win together. We rise as one. We support each other, raise the bar, and celebrate collective success. As the first PM you set a standard the rest of the team inherits, and the milestones belong to the team rather than to product.
Cross the aisle. Collaboration wins. We listen deeply, work across boundaries, and prioritize shared success over individual lanes. The best product calls here come from engineers who've sat with underwriters and underwriters who understand where the model breaks, and much of this job is listening closely enough on both sides to make that happen.
Go get it. We act with urgency, move with confidence, take smart risks, and push forward with intention. Nobody hands you the roadmap, the data, or the meeting invite. You pull the failing runs, book the time with the underwriters, and decide what matters.
Benefits
Premium Healthcare
100% contribution to top-tier health, dental, and vision
Fertility benefits and family building support
Unlimited PTO
Flexibility to take the time off, recharge, and perform
Daily lunches, dinners, and snacks
We work together, and enjoy meals together too
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
Plenty of dogs to play with and make friends with in the SF office