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Mandip Lamsal

@mandiplamsal

Senior Data Engineer at Change Healthcare building Azure healthcare data platforms and a RAG compliance search system that cut document review time by 40%.

United States

Message

At Change Healthcare, I optimize PySpark and Scala pipelines on Azure Databricks for healthcare datasets, reducing processing time by 35% and supporting more than 50 BI reports. I also built batch and real-time ingestion frameworks and a RAG-powered compliance document search system that reduced manual review time by approximately 40%.

Previously, I developed financial data pipelines and Lakehouse models at Capital One, healthcare ETL and analytics at Signify Health, and batch and streaming data workflows at Goldman Sachs. My work spans Azure and AWS platforms, with a focus on reliable data processing, governance, and analytics.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Change Healthcare

Sep 2024 - Present (2 years 1 month)

Optimized large scale PySpark and Scala ETL pipelines on Azure Databricks for petabyte scale healthcare datasets, reducing processing time by 35%. Built scalable batch and real-time data ingestion frameworks processing 10TB+ daily claims, billing, and clinical data streams.

Data Engineer

Capital One

May 2022 - Jul 2024 (2 years 2 months)

Developed enterprise scale ETL/ELT pipelines using Azure Data Factory, Azure Databricks, PySpark, SQL, and ADLS Gen2 to process high volume financial datasets. Implemented Medallion Lakehouse Architecture and incremental/CDC pipelines for efficient data processing.

ETL Developer

Signify Health

Feb 2020 - Apr 2022 (2 years 2 months)

Designed and developed scalable ETL/ELT pipelines using AWS Glue, Python, SQL, PySpark, and Amazon S3 to process healthcare claims and clinical data. Implemented S3-based data lake architecture and optimized analytical datasets in Amazon Redshift.

Big Data Engineer

Goldman Sachs

May 2018 - Jan 2020 (1 year 8 months)

Developed high volume batch ETL pipelines using Python, SQL, PySpark, Hadoop, and Hive to process financial transactions and market data. Optimized Spark and Hive workloads, improving processing performance by up to 60%.