Surendra Kumar Varma

About Surendra Kumar Varma

Data Engineer with 5+ years of experience designing, building, and optimizing large-scale ETL and real-time analytics solutions in financial services and manufacturing. Proven expertise in building scalable data pipelines, architecting cloud-native platforms (AWS, GCP), and leveraging big data tools like Apache Spark, Kafka, Hadoop, and NiFi.At Freddie Mac, I led initiatives to modernize data infrastructure, reduce data latency by 40%, and support regulatory compliance with CCAR and Basel At KPIT, I built cloud-native data architectures on AWS & Snowflake to handle 5TB+ daily IoT data from manufacturing units, improving system reliability and analytics throughput.Skilled in Python, SQL, SAS, and a range of analytics and visualization tools including Power BI and Tableau. Passionate about driving actionable insights, improving data reliability, and enabling intelligent decision-making across enterprises.Open to challenging opportunities in Data Engineering, Cloud Data Platforms, or Analytics Infrastructure roles across the U.S. and globally.Let’s connect if you’re hiring or looking to collaborate!

Experience

1. Data Engineer

   Freddie Mac

   Jan 2024 — Present · US

   Designed and optimized large-scale ETL pipelines using Apache Spark, Airflow, Talend, NiFi, and Informatica to process over 500GB of financial, credit, and risk data daily, reducing batch latency by 40% and enabling near real-time post-trade and loan-level analytics. Developed and supported streaming data architectures with Apache Kafka and Spark Streaming for real-time fraud detection on high frequency loan transactions, reducing false positives by 15% and enhancing risk mitigation strategies. Architected scalable data platforms using Hadoop, Delta Lake, and AWS Databricks integrated with HDFS and Amazon S3, supporting petabyte-scale analytics for mortgage asset valuation, borrower behavior analysis, and regulatory compliance. Conducted predictive modeling and advanced analytics using Python, R, SQL, SAS, and libraries such as Pandas and Seaborn to drive credit risk scoring, loan performance prediction, anomaly detection, and portfolio optimization. Led migration of legacy Oracle and SQL Server databases to AWS RDS and GCP BigQuery, improving query performance by 40%, reducing infrastructure cost by 25%, and modernizing Freddie Mac’s data environment to a cloud-native architecture. Delivered high-performance dashboards and automated reporting solutions using PySpark, Hive, and Tableau, providing real-time insights into trading performance, loan pipeline health, and operational risk to stakeholders and senior executives. Managed distributed database systems including MySQL, MongoDB, HBase, and DynamoDB, performed Hadoop cluster tuning, access control enforcement, and regulatory data governance under CCAR, Basel and SOX compliance mandates.

Education

• KL University

  Bachelor of Technology

  2016 — 2020

• University of Houston-Clear Lake

  Master