Lead Azure Data Analyst with Property Insurance/Reinsurance background

Role: Lead Azure Data Analyst with Property Insurance/Reinsurance background
Location: Princeton, NJ (Day 1 onsite, minimum 3 days office)
Duration: Long term contract

Key Skills: Azure Data Analyst, Azure Data Factory, Power BI, Azure Databricks, Azure Analysis Services, SQL Server Integration Services, SQL Server Reporting Services, Azure Data Factory(ADF)
looking for someone with strong property insurance background (Eg: Property domain - oil rig company- flood zone, earthquake zone - brick/wood material properties) (client will not consider the profiles without this experience)

Key Notes:
Data ingestion, engineering on Azure using Databricks notebooks, ADF etc.
Experience dealing underwriting in Insurance

Job Description:
Bachelor’s degree with 13-15+ years of experience
Experience in implementing Microsoft BI/Azure BI solutions like Azure Data Factory, Power BI, Azure Databricks, Azure Analysis Services, SQL Server Integration Services, SQL Server Reporting Services.
Good Understanding of Azure Big data technologies like Azure Data Lake Analytics, Azure Data Lake Store, Azure Data Factory and created POC in moving the data from flat files and SQL Server using U-SQL jobs.
Good knowledge in implementing various business rules for Data Extraction, Transforming and Loading (ETL) between Homogenous and Heterogeneous Systems using Azure Data Factory(ADF).
Involved in creating the notebooks for moving data from raw to stage and then to curated zones using Databricks.
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
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.