adarsh dameruppula

At Bank of America, I build high-scale ETL/ELT pipelines and dimensional models that process billions of financial records. Using PySpark, Airflow, AWS Glue, and Amazon EMR, I improved pipeline performance by 45% and reduced processing time by 30%.

I architected a serverless lakehouse using Amazon S3, Snowflake, and Amazon Redshift Spectrum, enabling sub-second queries for more than 50 operational and finance divisions. I also built Kinesis, Kafka, and Flink streaming pipelines that reduced end-to-end data latency by 70%.

Previously at S&P Global, I automated high-volume financial-data ingestion and transformation on AWS with Python, SQL, and Airflow, reducing pipeline latency by 40%. At Genpact, I analyzed healthcare claims data, built Snowflake pipelines, and helped reduce claim rejection rates by 18%.

I focus on documented, governed data products that teams can trust. My work spans dbt, Snowflake, AWS, data quality, lineage, CI/CD, and semantic models that support self-service analytics and real-time decision-making.