Senior Azure Data Engineer
Current• Worked with data transfer from on-premises SQL servers to cloud databases (Azure Synapse Analytics (DW) and Azure SQL DB). • Created Pipelines that were built in Azure Data Factory using Linked Services/Datasets/Pipeline/ to extract, transform, and load data from a variety of sources including Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool, and reverse. • Created CI-CD Pipelines using Azure devOps.• Used a blend of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics, gather, convert, and load the data from source systems to Azure Data Storage services. • Processed structured and semi-structured data into Spark Clusters using Spark SQL and Data Frames API. • Created Spark apps with Azure Data Factory and Spark-SQL for data extraction, transformation, and aggregation from various file formats in order to analyze and transform the data in order to reveal insights into consumer usage patterns. • Ingestion of data into one or more Azure Services (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing of data in Azure Databricks. • Monitored the SQL scripts and modified them for improved performance using PySpark SQL. • Managed relational database service in which Azure SQL manages scalability, stability, and maintenance. • Integrated data storage options with Spark, notably with Azure Data Lake Storage and Blob storage. • Experience in ETL jobs and developing and managing data pipelines. • Experience in tuning hive jobs by performing partitioning, bucketing, and optimized joins on hive tables. • Performed data transformations and analytics on large dataset using Spark. • Created highly interactive Data Visualization Reports and Dashboards using feature such as Data Blending, Calculations, Filters, Actions, Parameters, Maps, Extracts, Context Filters, Sets and Aggregate measures using Tableau.• Combined views and reports into interactive dashboards in Tableau Desktop.