Data Engineer
Current● Developed data processing ETL pipelines using PySpark, involving data reading from external sources, merging, data enrichment, and loading into Azure Sql data warehouse.● Developed PySpark scripts to process streaming data from data lakes using Spark Streaming, enabling real-time data processing capabilities, and performed data transformations, cleaning, and filtering with Spark Data Frame API to efficiently load processed data into Hive.● Leveraged Hive Meta data store backup, partitioning, and bucketing techniques to optimize Spark job performance and tuning.● Possess a strong understanding of Spark Architecture, including Spark Core, Spark SQL, Data Frames, Spark Streaming, and related components such as Driver Node, Worker Node, Stages, Executors, and Tasks.● Handled complex Hive queries, conducting table joins for extracting meaningful insights related to Spark jobs.● Converted Hive SQL queries into Spark transformations using Spark Data Frames and Python, analyzing SQL scripts to design efficient PySpark solutions. Conducted performance tuning of Spark Applications, optimizing Batch Interval time, Parallelism, and memory settings for enhanced efficiency.● Implemented hybrid connectivity between Azure and on-premises environments using virtual networks, VPN, and Express Route.● Successfully migrated SQL databases to Azure Data Lake, Azure SQL Database, Data Bricks, and Azure SQL Data Warehouse, using Apache Airflow ensuring seamless database access and migration. ● Implemented Azure Data Factory (ADF) extensively for ingesting data from different source systems like rational and unstructured data to meet business functional requirements.● Configured Snowpipe for the continuous data flow from the Azure datalake to the external stage to the Snowflake staging layer.● Implemented end-to-end data extraction, transformation, and loading (ETL) processes using Azure Data Factory (ADF) and Azure HDInsight.