Rohit Pradhan
Data Engineer @Bank of America
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WORK HISTORY
Data Engineer @Bank of America
US
FRTB Cloud Migration (Databricks POC)Contributed to Bank of America’s first on-prem to Databricks cloud migration proof-of-concept under the FRTB regulatory program, focusing on end-to-end data migration and performance optimization.Designed and implemented data pipelines to migrate multi-source banking data from Impala/HDFS to ADLS Gen2 using Azure Data Factory, and built Delta tables within Databricks to support analytical workloads.Configured Unity Catalog for centralized governance and access control, improving data lineage visibility and audit readiness to meet regulatory requirements.Migrated legacy Parquet and Iceberg datasets to Delta Lake, leveraging Delta logs and deletion vectors for faster incremental updates and efficient storage management.Enhanced performance through Delta table liquid clustering and Spark SQL query tuning, optimizing 75+ legacy Impala queries. Achieved a runtime reduction from 3 hours on-prem to under 7 minutes on Databricks while lowering compute costs.Developed data egress pipelines to deliver processed results back to on-prem systems, establishing a repeatable migration and governance pattern later used for broader enterprise adoption.
EDUCATION
Stevens Institute of Technology
Master of Science - MS, Machine Learning
Savitribai Phule Pune University
Bachelor of Engineering - BE, Electronics and telecommunication
ABOUT ROHIT PRADHAN
I’m a Data Engineer and Data Scientist who loves turning data complexity into clarity. Majorly, helping organizations modernize their data ecosystems, build scalable pipelines, and turn complex datasets into meaningful insights.At Bank of America, I led the bank’s first migration from on-premise systems to Databricks under the FRTB regulatory framework — cutting query runtimes from 3 hours down to just 7 minutes. My work focuses on building efficient and governed data platforms using Azure, ADLS Gen2, Unity Catalog, Delta Lake, and Spark to power analytics and machine learning at scale.I enjoy working across the entire data lifecycle — from ingestion and transformation to modeling and visualization — combining the precision of engineering with the creativity of analytics.My toolkit includes Python, SQL, Spark, Databricks, ADF, and a strong background in machine learning and statistical modeling.If you’re building intelligent, data-driven systems that balance scale with strategy—I’d love to connect.
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