Arun Vivek Supramanian is a Senior Data Engineer with over 14 years of experience in data engineering, data analysis, and application development. He has deep expertise in designing and maintaining large-scale data pipelines, cloud-based data solutions, and enterprise data warehouses. With 9+ years of Python development experience, he has built high-performance data workflows for big data processing and analytics.
At Amazon, Arun led the development of scalable data warehouses using Amazon Redshift, reducing data redundancy by 90% and cutting costs by 50%. He designed real-time data pipelines, improved scalability by 80%, and played a key role in launching Virtual Try-On and Try Before You Buy products. He also developed centralized data repositories, reducing processing inefficiencies by 70%, and created a Generative AI chatbot for knowledge management.
Before Amazon, Arun worked at Undertone, where he developed real-time ad campaign dashboards and optimized data pipelines, improving performance and reducing costs. He also built data lakes on AWS S3 and Athena to support machine learning pipelines and implemented AI-driven customer targeting models, increasing revenue by 30%.
His experience at NTechConnect involved building ETL workflows for processing large-scale structured and unstructured data into Oracle and developing cloud migration strategies. He designed self-service reporting platforms using Apache Spark and Impala, cutting manual reporting by 90% and enhancing HIPAA-compliant federated data layers.
Arun’s technical skills include Python, Scala, Java, SQL, and PL/SQL. He has hands-on experience with AWS services (Redshift, Glue, EMR, Lambda, S3, Athena), Hadoop, Spark, Hive, and Impala. He has designed and implemented enterprise data architectures for large-scale data processing and analytics.
He holds a Master of Science in Computer Science from New York University and a Bachelor of Engineering in Computer Science from Anna University, India. His work has consistently led to cost reductions, improved data efficiency, and revenue growth in data-driven organizations.