Sr Data Engineer at GE Healthcare

Designed and implemented end-to-end Big Data processing pipelines using Hadoop ecosystem components (HDFS, MapReduce, Pig, Hive) and Spark. Migrated on-premise data processing to cloud-based architectures including Azure Synapse Analytics, Azure SQL DB, and Snowflake. Built robust data pipelines with Azure Data Factory, Azure Data Lake Storage Gen2, and Azure Databricks. Developed Spark applications in Scala and PySpark for data extraction, transformation, and validation. Leveraged Snowflake features (Time Travel, Streams & Tasks) and built data integration across multi-cloud environments. Created automated Python ETL scripts, orchestrated pipelines with Dataproc/Airflow, and supported data ingestion from APIs, flat files, and databases. Led cross-cloud data migration initiatives to GCP (BigQuery) and designed dimensional models (Star Schema) and OLAP cubes for analytics. Implemented data governance workflows, MDM, profiling, and performance tuning; used Kafka for real-time streaming.

Data Engineer at Nationwide Insurance

Participated in Agile sprints; designed RESTful APIs using Python (Flask) for data access; worked with AWS services (EC2, S3, IAM, Lambda, VPC, ELB) for deployment and compute. Built Spark-based pipelines for streaming and batch processing into HDFS and S3; used AWS Glue for ETL and migrated Hive metadata to AWS Glue Catalog; automated data workflows with Jenkins and Docker, deploying microservices to Kubernetes. Implemented data syncing between MongoDB and PostgreSQL; optimized Hive queries via partitioning and bucketing; designed ETL frameworks loading data into Redshift and Hive; authored complex SQL across Oracle/Teradata and created Star schemas; delivered Tableau dashboards for business users.