Data Engineer needs 10 years experience Data Engineer requires: Business Intelligence:Azure Data Factory (ADF), Azure Databricks, Azure Analysis Services (SSAS), Azure Data Lake Analytics, Azure Data Lake Store (ADLS), Azure Integration Runtime, Azure Event Hubs, Azure Stream Analytics, DBT Database Technologies:Azure SQL, MongoDB, PySpark Familiarity with reinsurance broking data, including placements, treaty structures, client hierarchies, and renewal workflows. Understanding of actuarial rating inputs and outputs, including exposure and experience data, layers, tags, and program structures. Experience building data pipelines that support actuarial analytics, pricing tools, and downstream reporting for brokers and client Expert in data warehouse development starting from inception to implementation and ongoing support, strong understanding of BI application design and development principles using Normalization and De-Normalization techniques. Experience in developing staging zone, bronze, silver and gold layers of data Good knowledge in implementing various business rules for Data Extraction, Transforming and Loading (ETL) between Homogenous and Heterogeneous Systems using Azure Data Factory (ADF). Developed notebooks for moving data from raw to stage and then to curated zones using Databricks. Involved in developing complex Azure Analysis Services tabular databases and deploying the same in Microsoft azure and scheduling the cube through Azure Automation Runbook. Extensive experience in developing tabular and multidimensional SSAS Cubes, Aggregation, KPIs, Measures, Partitioning Cube, Data Mining Models, deploying and Processing SSAS objects. Experience in Agile software development and SCRUM methodology. SDLC Technical writing