Principal Data Engineer

Summary

Ledgebrook is building a modern Excess & Surplus (E\&S) lines insurance company that pairs deep underwriting and pricing expertise with a forward-looking data platform. As Principal Data Engineer, you will set the technical direction for our data platform, own architecture decisions across Snowflake, dbt, Airflow, Terraform, Python and SQL, and enable scalable underwriting intelligence, pricing models, and operational analytics. This role offers high-impact ownership, close collaboration with actuarial, underwriting, finance and product leaders, and the chance to raise engineering standards across the organization.

Responsibilities

• Architect the end-to-end data platform and make foundational decisions on pipeline architecture, data modeling patterns, and platform evolution across Snowflake, dbt, Airflow, and Terraform.
• Define and enforce engineering standards for data quality, testing, observability, and deployment that become team defaults.
• Design semantic layers and data models to support underwriting, finance, and executive decision-making while keeping models accessible for analysts.
• Own data governance posture including data contracts, SLAs, lineage, and documentation to ensure trusted data is a property of the system.
• Partner with data science and engineering leadership to ensure the platform supports advanced analytics, ML pipelines, and AI initiatives.
• Mentor engineers, run rigorous code and design reviews, and close skill gaps to elevate team capability and output.
• Drive cross-functional alignment on technical direction by working directly with actuarial, underwriting, finance, and product leaders.
• Identify gaps or failures in systems, propose pragmatic solutions, and lead delivery through to production.
• Operate effectively in high-ambiguity situations, defining the path forward and iterating on solutions as needed.

Requirements

• Principal-level ownership of platform architecture decisions end-to-end, including design, tradeoffs, delivery, and iteration.
• 10+ years of experience in data engineering or analytics engineering.
• Expert-level command of Python and SQL with production systems others maintain and build on.
• Deep hands-on experience with Airflow, dbt and Snowflake (or equivalent) in production environments.
• Extensive experience with AWS and Terraform.
• Proven experience building and enforcing data quality, observability, and governance frameworks at scale.
• Track record of driving cross-functional alignment on technical direction without relying on positional authority.
• Comfort operating in high-ambiguity environments where you define the path, not just follow one.

Nice to Have

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