Job Description

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.
• SDLC
• Technical writing

• Experience in Agile software development and SCRUM methodology.

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