Principal Data Engineer
Description
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 .
About the Role
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.
What You’ll Do:
- 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.
About you
We love working with people who are eager to learn constantly and genuinely excited about the chaotic, rewarding process of building a company from the ground up.
You’ll be a great fit if you bring:
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Low Ego, High Impact. You care more about getting it right than being right, and you have the credibility to back it up.
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Strong Opinions, Weakly Held . You bring a clear point of view and defend it with evidence, but you update fast when the data changes.
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Builder Mentality . You'd rather ship the solution than write a deck about it. You're still writing code.
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Multiplier, Not Just Contributor. You measure your success by what the team ships, not just what you built yourself.
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Team First. You want to win, but only if we win together.
Requirements
Must Haves:
- 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.