Qualifications Associate degree or an equivalent combination of education and experienceCertification in Databricks engineeringThree or more years in data engineering, including one or more years hands-on with Databricks (PySpark, Spark SQL, Delta Lake)Experience with property and casualty insurance data, ideally Guidewire data; experience migrating data from legacy source systemsKnowledge of Python/PySpark and SQL (Structured Query Language)Knowledge of Unity Catalog: permissions, row/column-level security and lineageExperience with incremental loading patterns such as CDC, MERGE, SCD (Slowly Changing Dimension) and Auto LoaderKnowledge of data modeling, including dimensional modelingExperience scheduling and monitoring pipelines with Databricks Workflows and/or Azure Data FactoryExperience with Git-based version control (GitHub or Bitbucket), including branching, pull request reviews and Databricks Git foldersExperience with Azure data services such as ADLS (Azure Data Lake Storage) Gen2, Azure Data Factory and Key VaultKnowledge of CI/CD basics (Databricks Asset Bundles, Azure DevOps, GitHub Actions or Bitbucket Pipelines)Experience with streaming (Structured Streaming, Event Hubs)Experience with API (Application Programming Interface) and integration tools such as MuleSoft is an added advantage Lets talk benefits! 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.