Abhilash Bandari

- Developed data ingestion frameworks to integrate structured and semi-structured data from multiple sources- Designed and implemented scalable ETL pipelines using AWS Glue and PySpark to process 500GB+ healthcare data daily

- Optimized Spark jobs to enhance processing performance by 40%, resulting in faster data availability for analytics

- Built and maintained data warehouse solutions on Amazon Redshift to enable efficient data retrieval and reporting

- Implemented data quality validation reducing data errors by 35%

- Automated workflows using Apache Airflow reducing manual effort by 50%

- Collaborated with analytics teams to deliver reporting and dashboard requirements, supporting data-driven decision making

- Developed scalable data pipelines using Azure Data Factory and Databricks

- Processed large datasets exceeding 300GB daily using Spark

- Designed and implemented data warehouse using Azure Synapse

- Improved data load performance by 30% through query optimization

- Built incremental data processing pipelines

- Implemented monitoring and alerting mechanics

- Worked closely with BI teams to deliver reporting solutions

- Improved ETL reliability and reduced failures by 25%

- Developed ETL pipelines using Python and SQL

- Integrated multiple financial data sources into centralized warehouse

- Designed database schemas data model

- Performed performance tuning on SQL queries and

- Supported data migration and transformation activities