Principal Data Engineer

At Ledgebrook, we are building an Excess & Surplus (E&S) lines insurance company that combines deep underwriting and pricing expertise with a modern tech platform fit for the future of insurance– truly a best of both worlds approach. Our speed and service underpin every decision we make, and our rapidly growing team is a testament to our value proposition resonating with the market. You bring the passion and entrepreneurial spirit, and we’ll provide the opportunity to unleash the very best of your talents and skills. Apply now to advance your career at Ledgebrook.

We're seeking a Staff Data and Analytics Engineer who operates at the intersection of deep technical craft and organizational leverage. You'll set the technical direction for our data platform, raise the bar for how we build, and make everyone around you more effective.

You'll own the architecture, establish the standards others build on, and drive outcomes that span engineering, actuarial, finance, and product. When something is broken or missing, you're the one who identifies it, proposes the solution, and sees it through.

You'll work in a modern data stack with python, Airflow, dbt, Terraform, and Snowflake. Your decisions will shape how we scale underwriting intelligence, pricing models, and operational infrastructure for years.

• →Architect the Data Platform – Own the end-to-end design of our data infrastructure. Make the foundational calls on pipeline architecture, data modeling patterns, and platform evolution across Snowflake, dbt, Airflow, and Terraform.
• →Define Engineering Standards – Establish and enforce practices around data quality, testing, observability, and deployment that the rest of the team builds on. Your patterns become the defaults.
• →Enable Data-Driven Decisions at Scale – Design semantic layers and data models complex enough to support underwriting, finance, and executive strategy. Simple enough that analysts can use them without hand-holding.
• →Drive Data Governance – Own the governance posture: data contracts, SLAs, lineage, and documentation. Make trusted data a property of the system, not a manual effort.
• →Shape the ML and AI Foundation – Partner with data science and engineering leadership to ensure the platform supports advanced analytics, ML pipelines, and AI initiatives.
• →Elevate the Team – Mentor engineers, conduct rigorous code and design reviews, and actively close skill gaps. Your leverage is measured in part by how much better the people around you get.
• →Partner at the Leadership Level – Engage directly with actuarial, underwriting, finance, and product leaders to translate business complexity into technical roadmap. You're a peer to stakeholders, not a ticket-taker.

• 8+ years of experience in data engineering or analytics engineering
• Expert-level command of Python and SQL, with a history of production systems that others maintain and build on.
• Deep hands-on experience with Airflow, dbt, Terraform, and Snowflake (or equivalents) in production environments.
• Demonstrated ability to own platform architecture decisions end-to-end: design, tradeoffs, delivery, and iteration.
• Track record of driving cross-functional alignment on technical direction without relying on positional authority.
• Experience building and enforcing data quality, observability, and governance frameworks at scale.
• Comfort operating in high-ambiguity environments where you define the path, not just walk it.

• Experience in insurance, fintech, or other regulated domains.
• Familiarity with Power BI, Tableau, Looker, or other BI tools.
• Experience designing for compliance and data governance requirements (SOC 2, CCPA, etc.).
• Prior experience building data infrastructure at a high-growth startup for growth stage.

• Languages: Python, SQL
• Data Stack: Snowflake, dbt, Apache Airflow (AWS MWAA)
• Cloud Infrastructure: AWS, Terraform
• Tools: GitHub, Jira, Confluence, Slack