Krishna Prasad Chapagain

Migrated legacy batch-processing workloads to a fully managed orchestration framework based on Apache Airflow, establishing complex DAG dependencies that improved overall pipeline reliability and fault tolerance by 99%. Utilized an enterprise data catalog to maintain structure around files and improve data quality within parquet files, and implemented serverless SQL query engines for ad hoc queries and reports. Developed optimized PySpark scripts for pre-processing and transformation of data required for analytics on distributed cloud compute clusters.

Senior Data Engineer at WNS Analytics (2025-02 – 2025-08)

• Migrated legacy batch-processing workloads to a fully managed orchestration framework based on Apache Airflow, establishing complex DAG dependencies that improved overall pipeline reliability and fault tolerance by 99%.
• Utilized an enterprise data catalog to maintain structure around files and improve data quality within parquet files, and implemented serverless SQL query engines for ad hoc queries and reports.
• Developed optimized PySpark scripts for pre-processing and transformation of data required for analytics on distributed cloud compute clusters.

Apprentice Leader – Data Engineer, BFSI Division at Mu Sigma Inc. (2024-01 – 2025-02)

• Managed and maintained a data warehouse using Databricks, originated a cloud-based data ingestion system leveraging a managed database migration service to transfer sensitive claims data from a centralized cloud data warehouse to a scalable object storage data lake, satisfying ARPC regulatory requirements with 99% data accuracy.
• Orchestrated a data engineering training initiative centered on enterprise cloud data architectures, ETL pipelines, and distributed storage, mentoring 700+ new hires and shortening client solution delivery readiness by 25%.
• Architected a near real-time data streaming pipeline leveraging managed event-streaming services to ingest high-velocity financial transactions into an object storage data lake, reducing downstream data availability latency from hours to sub-minutes.

Decision Scientist - Data Engineer at Mu Sigma Inc. (2021-01 – 2024-01)

• Engineered a predictive analytics model in a managed cloud machine learning platform using LightGBM, achieving 86% accuracy in initial claim reserve forecasting; reduced claims processing times by 20% by integrating transformed data and automated feature importance workflows.
• Designed and deployed scalable data visualization pipelines connecting cloud data stores to 4 interactive Tableau dashboards; streamlined stakeholder data access and reduced finance team report generation time by 35%.
• Automated data ingestion and transformation pipelines using serverless compute functions and Python, increasing pipeline efficiency by 42% and eliminating 5 hours of manual operational effort per week.

Master of Science in Management Information Systems (STEM) – University of Illinois Chicago, UIC Business (2026-12)

Masters in Data Science and Engineering – Birla Institute of Engineering, BITS

Bachelor of Technology in Computer Science – Visvesvaraya Technological University (2020-04)