Databricks Data Engineer

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    "url": "https://careers-berkley.icims.com/jobs/13782/databricks-data-engineer/job",
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    "title": "Databricks Data Engineer",
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    "industry": "Financial ServicesInsurance",
    "datePosted": "2026-03-25T04:00:00.000Z",
    "description": "<h2>Company Details</h2>\n<p></p>\n<p> </p>\n<p>We started in early 2019 as a small group of technologists with a passion for making insurance better. Today we are working with a team of industry experts who run five different insurance brands and collectively control $1 billion in annual premiums.</p>\n<p> </p>\n<p>We believe in an idea and execution meritocracy. In other words, a place where the best ideas win and the people who deliver the most value get the most opportunities.</p>\n<p> </p>\n<p>As we grow our team, we are looking for inquisitive, entrepreneurial people who are excited to reimagine the insurance industry.</p>\n<p> </p>\n<p>Insurance is too complex. Help us make it better.</p>\n<h2>Responsibilities</h2>\n<p> </p>\n<em><strong>This position requires on-site work Monday–Thursday at either our Manassas, VA or Chesterfield, MO location. </strong></em>\n<p> </p>\n<p> </p>\n<p>The<strong> Databricks Data Engineer </strong>will help design, build, deploy, and maintain scalable and production grade data pipelines in modern cloud environments, enabling analytics, AI, ML, and decision advantage at scale.  This role will work with cutting-edge tools like Databricks, Delta Lake, PySpark, and AI/BI genie to transform raw data into actionable insights.  As a hands-on Databricks Data Engineer with deep expertise in Azure Databricks and MLOps, this role will have the opportunity to migrate and translate legacy SSIS ETL logic into scalable, cloud-native data pipelines in Databricks. This role will partner with data engineers, data scientists, and product manager to design features, train/evaluate models, and deploy them to production using MLflow, Databricks and Workflows—with rigorous observability, governance (Unity Catalog), and CI/CD automation.</p>\n<p> </p>\n<p><strong>Data Pipeline Engineering</strong></p>\n<p> </p>\n<ul>\n <li>Design, build, and maintain high-performance, scalable ETL/ELT pipelines using Azure Databricks, Delta Lake, and PySpark.</li>\n <li>Convert and modernize existing SSIS package logic into cloud-native Databricks pipelines using PySpark notebooks, Delta Live Tables (DLT), and Databricks Workflows.</li>\n <li>Implement reliable batch and streaming pipelines with robust data quality and validation frameworks.</li>\n <li>Optimize pipeline performance using Photon, efficient file formats, partitioning, Z-ordering, and caching strategies.</li>\n</ul>\n<p> </p>\n<p><strong>Lakehouse Platform Development</strong></p>\n<p> </p>\n<ul>\n <li>Develop and manage datasets within Delta Lake, ensuring ACID reliability, schema evolution, versioning, and time travel.</li>\n <li>Architect feature-rich data layers including:\n  <ul>\n   <li>Bronze (raw ingestion)</li>\n   <li>Silver (validated, conformed)</li>\n   <li>Gold (analytics-ready and ML-ready)</li>\n  </ul></li>\n <li>Implement data governance using Unity Catalog for fine-grained access control, lineage, auditability, and metadata management.</li>\n</ul>\n<p> </p>\n<p> </p>\n<p><strong>MLOps & ML-Enabled Data Pipelines</strong></p>\n<p> </p>\n<ul>\n <li>Partner with data scientists and data engineers to create feature pipelines, model training pipelines, and production scoring pipelines.</li>\n <li>Deploy and operationalize models using MLflow, Databricks Model Registry, and Databricks Workflows.</li>\n <li>Use Databricks built-in AI SQL functions such as ai_query, ai_forecast, ai_analyze_sentiment to generate actionable insight from large amount of unstructured or structured raw data</li>\n <li>Implement monitoring for:\n  <ul>\n   <li>Pipeline failures</li>\n   <li>Data/feature drift</li>\n   <li>Model performance degradation</li>\n   <li>Operational SLAs/SLIs/SLOs</li>\n  </ul></li>\n <li>Build automated CI/CD workflows using GitHub Actions or Azure DevOps for notebook deployment, pipeline testing, and environment promotion.</li>\n</ul>\n<p> </p>\n<p><strong>Data Platform, Data Security & Data Governance</strong></p>\n<p> </p>\n<ul>\n <li>Collaborate with data engineers to design reliable data products on <strong>Delta Lake</strong>; leverage <strong>Delta Live Tables (DLT)</strong> for declarative pipelines when applicable.</li>\n <li>Enforce <strong>Unity Catalog</strong> for lineage, permissions, and audit; manage secrets, tokens, and keys securely (e.g., <strong>Databricks secrets</strong>, <strong>Key Vault/Secrets Manager</strong>).</li>\n</ul>\n<p> </p>\n<p><strong>Collaboration & Leadership</strong></p>\n<p> </p>\n<ul>\n <li>Work closely with cross-functional teams: data engineering, data scientist, product manager, and business stakeholders.</li>\n <li>Serve as a Databricks SME—championing best practices, code standards, governance, and reusable frameworks.</li>\n <li>Document architecture, workflows, data models, runbooks, and operational procedures.</li>\n</ul>\n<p> </p>\n<h2>Qualifications</h2>\n<ul>\n <li>Minimum of 3 years of experience in Databricks, PySpark notebooks, Python, DevOps, software development, and data engineering.</li>\n <li>Certified Databricks Data Engineer Associate or Professional is a plus.</li>\n</ul>\n<p><strong> </strong></p>\n<p><strong>Skills & Competencies</strong></p>\n<ul>\n <li>Proficient in designing, building, deploying, and maintaining high-performance, scalable ETL/ELT pipelines using Azure Databricks, Delta Lake, and PySpark Notebook.</li>\n <li>Proficient in building, deploying, and operating production ML models such as supervised, unsupervised, and anomaly detection, including techniques for imbalanced datasets</li>\n <li>Proficient with ML engineering and MLOps, including model versioning, CI/CD for ML, monitoring, drift detection, and automated retraining</li>\n</ul>\n<ul>\n <li>Proficiency in Python including Pandas and PySpark Dataframes</li>\n <li>Expert level of SQL skills including Stored Procedure, experience with SSIS, SSRS, Power BI is a plus.</li>\n</ul>\n<ul>\n <li>Proficient with cloud data engineering platforms, such as Azure, Databricks, Spark, or SQL, and batch and streaming pipelines</li>\n <li>Familiar with Databricks AI Built-In Functions such as AI_Query, AI_Gen, AI_Classify, AI_Forecast, AI_Analyze_Sentiment, able to use them to extract actionable insights from large amount of unstructured or structured raw data</li>\n <li>Experience with Python and ML frameworks, such as PyTorch or TensorFlow</li>\n <li>Experience improving data quality, lineage, and observability in enterprise data environments and operationalizing rules and model-driven scoring for prioritization, routing, or case selection</li>\n <li>Experience with predictive analytics, machine learning and artificial intelligence desired.</li>\n</ul>\n<p><strong>Education</strong></p>\n<ul>\n <li>A Bachelor’s degree in Computer Science, Management Information Systems, Engineering, Math, Physics, or a related quantitative field is required (4-year degree). Master’s degree preferred</li>\n <li>Experience in the commercial insurance industry is a plus.</li>\n</ul>\n<p><strong> </strong></p>\n<h2>Additional Company Details</h2>The Company is an equal employment opportunity employer. We do not accept any unsolicited resumes from external recruiting firms. The company offers a competitive compensation plan and robust benefits package for full time regular employees. Base salary & Benefits include Health, dental, vision, life, disability, wellness, paid time off, 401(k) and profit-sharing plans. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.\n<h2>Additional Requirements</h2>• Ability to travel locally and nationally up to 5% of the time\n<h2>Sponsorship Details</h2>Sponsorship not Offered for this Role",
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    "responsibilities": " \r\nThis position requires on-site work Monday–Thursday at either our Manassas, VA or Chesterfield, MO location. \r\n \r\n \r\nThe Databricks Data Engineer will help design, build, deploy, and maintain scalable and production grade data pipelines in modern cloud environments, enabling analytics, AI, ML, and decision advantage at scale.  This role will work with cutting-edge tools like Databricks, Delta Lake, PySpark, and AI/BI genie to transform raw data into actionable insights.  As a hands-on Databricks Data Engineer with deep expertise in Azure Databricks and MLOps, this role will have the opportunity to migrate and translate legacy SSIS ETL logic into scalable, cloud-native data pipelines in Databricks. This role will partner with data engineers, data scientists, and product manager to design features, train/evaluate models, and deploy them to production using MLflow, Databricks and Workflows—with rigorous observability, governance (Unity Catalog), and CI/CD automation.\r\n \r\nData Pipeline Engineering\r\n \r\n- Design, build, and maintain high-performance, scalable ETL/ELT pipelines using Azure Databricks, Delta Lake, and PySpark.\r\n- Convert and modernize existing SSIS package logic into cloud-native Databricks pipelines using PySpark notebooks, Delta Live Tables (DLT), and Databricks Workflows.\r\n- Implement reliable batch and streaming pipelines with robust data quality and validation frameworks.\r\n- Optimize pipeline performance using Photon, efficient file formats, partitioning, Z-ordering, and caching strategies.\r\n \r\nLakehouse Platform Development\r\n \r\n- Develop and manage datasets within Delta Lake, ensuring ACID reliability, schema evolution, versioning, and time travel.\r\n- Architect feature-rich data layers including:\r\n- Bronze (raw ingestion)\r\n- Silver (validated, conformed)\r\n- Gold (analytics-ready and ML-ready)\r\n\r\n- Implement data governance using Unity Catalog for fine-grained access control, lineage, auditability, and metadata management.\r\n \r\n \r\nMLOps & ML-Enabled Data Pipelines\r\n \r\n- Partner with data scientists and data engineers to create feature pipelines, model training pipelines, and production scoring pipelines.\r\n- Deploy and operationalize models using MLflow, Databricks Model Registry, and Databricks Workflows.\r\n- Use Databricks built-in AI SQL functions such as ai_query, ai_forecast, ai_analyze_sentiment to generate actionable insight from large amount of unstructured or structured raw data\r\n- Implement monitoring for:\r\n- Pipeline failures\r\n- Data/feature drift\r\n- Model performance degradation\r\n- Operational SLAs/SLIs/SLOs\r\n\r\n- Build automated CI/CD workflows using GitHub Actions or Azure DevOps for notebook deployment, pipeline testing, and environment promotion.\r\n \r\nData Platform, Data Security & Data Governance\r\n \r\n- Collaborate with data engineers to design reliable data products on Delta Lake; leverage Delta Live Tables (DLT) for declarative pipelines when applicable.\r\n- Enforce Unity Catalog for lineage, permissions, and audit; manage secrets, tokens, and keys securely (e.g., Databricks secrets, Key Vault/Secrets Manager).\r\n \r\nCollaboration & Leadership\r\n \r\n- Work closely with cross-functional teams: data engineering, data scientist, product manager, and business stakeholders.\r\n- Serve as a Databricks SME—championing best practices, code standards, governance, and reusable frameworks.\r\n- Document architecture, workflows, data models, runbooks, and operational procedures.\r\n ",
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