Data Engineer

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    "industry": "Insurance",
    "datePosted": "2026-04-10T04:00:00.000Z",
    "description": "<h2>Company Details</h2>\n<p></p>\n<p><strong>\"Our Company provides a state of predictability which allows brokers and agents to act with confidence.\"</strong></p>\n<p> </p>\n<p>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.</p>\n<p> </p>\n<p>Along the way, we’ve 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.</p>\n<p> </p>\n<p>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.</p>\n<p> </p>\n<p><strong>The Company is an equal employment opportunity employer. </strong></p>\n<p> </p>\n<h2>Responsibilities</h2>\n<p>We are seeking a Data Engineer with strong engineering, coding, and problem‑solving skills to design, build, and operate data platforms that support actuaries, analytics, modeling, and AI‑enabled workflows.</p>\n<p>This role is suited to someone who is technically strong, comfortable working independently, and able to translate complexity into robust, well‑designed systems that others can rely on.</p>\n<p> </p>\n<p>The position emphasizes engineering rigor, high‑quality code, system reliability, and sound judgment over one‑off 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.</p>\n<p> </p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n <li>Write production‑quality code for data ingestion, transformation, orchestration, and monitoring.</li>\n <li>Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch or distributed processing workloads (e.g., Spark‑based pipelines).</li>\n <li>Partner with actuaries, analytics, data science, and business teams to enable modeling and AI uses.</li>\n <li>Apply AI‑assisted engineering approaches, including LLM‑enabled tools or agents, to improve data quality, observability, documentation, and productivity.</li>\n <li>Identify data quality issues, bottlenecks, and failure modes; design systems that are resilient and observable.</li>\n <li>Stay current with data engineering and AI platform advancements, evaluate new tools, and recommend adoption where appropriate.</li>\n <li>Apply professional skepticism and alternate approaches to validate data correctness, lineage, and assumptions.</li>\n <li>Communicate system design, trade‑offs, and limitations clearly to technical and non‑technical stakeholders.</li>\n <li>Provide support and guidance to others who are at earlier stages in their data engineering or AI journey.</li>\n</ul>\n<h2>Qualifications</h2>\n<ul>\n <li>4–7 years of relevant data engineering, software engineering, or technical experience. A Master’s degree in Data Engineering or Computer Science.</li>\n <li>Familiarity with cloud data platforms and distributed processing frameworks (e.g., Databricks, Snowflake, Spark, or similar), and modern data engineering tooling.</li>\n <li>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.</li>\n <li>Experience designing and operating data pipelines, data lakes/warehouses, or distributed data systems.</li>\n <li>Experience applying AI, machine learning, or LLM‑based tools to real engineering problems (e.g., building agents, calling model APIs, integrating AI into engineering workflows).</li>\n <li>Experience working with large or complex data flows and creating defensible system designs and implementation plans.</li>\n <li>Strong professional judgment, curiosity, and attention to detail.</li>\n</ul>\n<h2>Sponsorship Details</h2>Sponsorship not Offered for this Role",
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    "responsibilities": "We are seeking a Data Engineer with strong engineering, coding, and problem‑solving skills to design, build, and operate data platforms that support actuaries, analytics, modeling, and AI‑enabled workflows.\r\nThis role is suited to someone who is technically strong, comfortable working independently, and able to translate complexity into robust, well‑designed systems that others can rely on.\r\n \r\nThe position emphasizes engineering rigor, high‑quality code, system reliability, and sound judgment over one‑off solutions or purely mechanical implementations. \r\nWe 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.\r\n \r\nResponsibilities\r\n- Write production‑quality code for data ingestion, transformation, orchestration, and monitoring.\r\n- Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch or distributed processing workloads (e.g., Spark‑based pipelines).\r\n- Partner with actuaries, analytics, data science, and business teams to enable modeling and AI uses.\r\n- Apply AI‑assisted engineering approaches, including LLM‑enabled tools or agents, to improve data quality, observability, documentation, and productivity.\r\n- Identify data quality issues, bottlenecks, and failure modes; design systems that are resilient and observable.\r\n- Stay current with data engineering and AI platform advancements, evaluate new tools, and recommend adoption where appropriate.\r\n- Apply professional skepticism and alternate approaches to validate data correctness, lineage, and assumptions.\r\n- Communicate system design, trade‑offs, and limitations clearly to technical and non‑technical stakeholders.\r\n- Provide support and guidance to others who are at earlier stages in their data engineering or AI journey.",
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