Professional Summary: Data engineer with professional experience in the Development, Delivery, Maintenance of Data warehouse Applications and Big data Applications across multiple domains. Have hands-on ETL/ ELT pipelines, data modeling, data lake architecture, data warehouse, database design in a distributed environment utilizing big data tools on cloud-based platforms.Core Competence:• Hands-on experience on Unified Data Analytics with Databricks, Databricks Workspace User Interface, Managing Databricks Notebooks, Delta Lake with Python, Delta Lake with Spark SQL.• Expertise inf Spark Architecture with Databricks, Structured Streaming. Setting Up AWS and Microsoft Azure with Databricks, Databricks Workspace for Business Analytics, Manage Clusters In Databricks, Managing the Machine Learning Lifecycle.• Hands-on experience in using Hadoop ecosystem components like Hadoop, Hive, Pig, Sqoop, HBase, Cassandra, Spark, Spark Streaming, Spark SQL, Oozie, Zookeeper, Kafka, Flume, MapReduce framework, Yarn, Scala, and Hue.• Extensively worked on AWS services like EC2, S3, EMR, RDS, SageMaker, Athena, Glue Data Catalog, RDS(Aurora), Redshift, DynamoDB, Elastic Cache (Memcached & Redis) & Quick Sight, and other services of the AWS family.• Hands-on experience in Azure Development, worked on Azure web application, App services, Azure storage, Azure SQL Database, Virtual machines, Fabric controller, Azure AD.• Extensive knowledge in working with Azure cloud platforms (HDInsight, DataLake, DataBricks, Blob Storage, Data Factory, Synapse, SQL, SQL DB, DWH, and Data Storage Explorer). • Experience in building and architecting multiple Data pipelines, including ETL and ELT processes for Data ingestion and transformation in GCP using BigQuery, Dataproc, CloudSQL, Datastore• Extensive experience in working with NoSQL databases and their integration Dynamo DB, Cosmo DB, Mongo DB, Cassandra, and HBase. • Expertise in transforming business requirements into analytical models, designing algorithms, building models, developing Data mining, Data Acquisition, Data Preparation, Data Manipulation,Validation and Visualization, and reporting solutions that scale across a massive volume of structured and unstructured data.• Excellent knowledge about the architecture and components of Spark, and efficient in working with Spark Core, Spark SQL, Spark Streaming, and expertise in building PySpark and Spark-Scala applications for interactive analysis, batch processing, and stream processing.