Data Engineer

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Position Summary The Data Engineer is responsible for designing, developing, and maintaining data pipelines that power the companys Azure Databricks lakehouse for its property and casualty insurance business.

Using technologies such as Delta Lake, Unity Catalog, Lakeflow pipelines, and Databricks SQL, the Data Engineer delivers reliable, high-quality, well-tested, and well-documented data to support key business functions, including underwriting, claims, actuarial, and finance.

A key responsibility is developing and supporting data pipelines for the phased migration of data from Guidewire and legacy policy, claims, and billing systems into the Azure Databricks lakehouse.

The Data Engineer ensures solutions align with the Unity Catalog governance framework and adhere to the engineering standards and best practices established by the Data Engineering Lead.

The role also emphasizes knowledge sharing, technical ownership, and long-term sustainability by progressively assuming responsibility for contractor-developed pipelines, strengthening internal expertise, and building lasting in-house platform capabilities.

Responsibilities Build pipelines for the phased data migration from Guidewire (PolicyCenter, ClaimCenter, BillingCenter) and legacy systems into the lakehouse on Azure Databricks, including historical loads, incremental/CDC (Change Data Capture) loads and reconciliation.Develop bronze and silver layer transformations in PySpark and Spark SQL on Delta Lake, following the medallion architecture.Support modeling of property and casualty insurance data: policies, insured risks and exposures, coverages, endorsements, premiums, claims, reserves, payments and recoveries.Integrate third-party data sources such as geospatial and weather data.Schedule and run production pipelines with Databricks Workflows (Lakeflow Jobs) or Azure Data Factory, including job dependencies, parameters, retries and alerting; investigate and resolve pipeline failures.Apply Unity Catalog controls defined by the Data Engineering Lead and Data Governance lead, including permissions, PII (Personally Identifiable Information) masking, tags and lineage.Implement data quality checks, error handling and monitoring.Tune pipeline performance and cost, including partitioning, liquid clustering and right-sized job compute.Use Git version control (GitHub or Bitbucket) and CI/CD (Continuous Integration/Continu...