Data Engineer

Our Company provides a state of predictability which allows brokers and agents to act with confidence.

Founded in 1967 W. R. Berkley Corporation has grown from a small investment management firm into one of the largest commercial lines property and casualty insurers in the United States.

Along the way weve been listed on the New York Stock Exchange become a Fortune 500 Company joined the S&P 500 and seen our gross written premiums exceed $10 billion.

Today the Berkley brand comprises more than 60 businesses worldwide and is divided into two segments: Insurance and Reinsurance and Monoline Excess. Led by our Executive Chairman founder and largest shareholder William. R. Berkley and our President and Chief Executive Officer W. Robert Berkley Jr. W.R. Berkley Corporation is well-positioned to respond to opportunities for future growth.

The Company is an equal employment opportunity employer.

We are seeking a Data Engineer with strong engineering coding and problemsolving skills to design build and operate data platforms that support actuaries analytics modeling and AIenabled workflows.

This role is suited to someone who is technically strong comfortable working independently and able to translate complexity into robust welldesigned systems that others can rely on.

The position emphasizes engineering rigor highquality code system reliability and sound judgment over oneoff solutions or purely mechanical implementations.
We seek someone to challenge the status quo and find better ways to build and operate data systems. You will advocate for the thoughtful application of modern data engineering data science and AI approaches.

Responsibilities

• Write productionquality code for data ingestion transformation orchestration and monitoring.
• Design build and maintain reliable scalable data pipelines and data platforms including batch or distributed processing workloads (e.g. Sparkbased pipelines).
• Partner with actuaries analytics data science and business teams to enable modeling and AI uses.
• Apply AIassisted engineering approaches including LLMenabled tools or agents to improve data quality observability documentation and productivity.
• Identify data quality issues bottlenecks and failure modes; design systems that are resilient and observable.
• Stay current with data engineering and AI platform advancements evaluate new tools and recommend adoption where appropriate.
• Apply professional skepticism and alternate approaches to validate data correctness lineage and assumptions.
• Communicate system design tradeoffs and limitations clearly to technical and nontechnical stakeholders.
• Provide support and guidance to others who are at earlier stages in their data engineering or AI journey.

• 47 years of relevant data engineering software engineering or technical experience. A Masters degree in Data Engineering or Computer Science.
• Familiarity with cloud data platforms and distributed processing frameworks (e.g. Databricks Snowflake Spark or similar) and modern data engineering tooling.
• Strong programming skills particularly in Python and SQL (including experience with distributed or batch processing frameworks such as PySpark or equivalent) with an emphasis on maintainable testable code.
• Experience designing and operating data pipelines data lakes/warehouses or distributed data systems.
• Experience applying AI machine learning or LLMbased tools to real engineering problems (e.g. building agents calling model APIs integrating AI into engineering workflows).
• Experience working with large or complex data flows and creating defensible system designs and implementation plans.
• Strong professional judgment curiosity and attention to detail.