Akash Reddy

About Akash Reddy

With over 10 years of professional experience, my mission as a Senior AWS Data Engineer at U.S. Bank is to leverage advanced AWS technologies to drive efficiency and innovation. My expertise in building and optimizing data pipelines aligns with the bank\'s commitment to technological advancement and customer satisfaction. I am dedicated to enhancing performance and reducing costs, bringing a unique perspective and diverse experience that fosters growth and efficiency in our team\'s endeavors.At U.S. Bank, my focus has been on implementing AWS data pipelines using Glue, S3, and Lambda, achieving a significant reduction in processing time and infrastructure costs. My role has involved streamlining batch data workflows and developing ETL solutions with Spark SQL in Databricks, leading to improved data processing efficiency and accuracy. These efforts are emblematic of my dedication to automating data ingestion and management, ensuring our systems scale effectively while maintaining robust security standards.

Experience

1. Aws Data Engineer Ai Ml Pipelines

   Ameriprise Financial Services, LLC

   Oct 2024 — Present

   Architected and led the full migration of enterprise SAS analytics workloads to an AWS Data Lake (S3 + Glue 4.0 + Athena), enabling ML-ready datasets and reducing analytics query costs by 40%.• Migrated legacy SAS analytics workloads to an AWS-based Data Lake, using Amazon S3, AWS Glue, and Amazon Athena to build scalable ingestion pipelines, enforce schema-on-read, and expose curated Gold-layer datasets for downstream ML and BI consumption.• Designed and implemented feature engineering pipelines using PySpark on Amazon EMR, producing training-ready datasets, behavioral aggregations, and time-window features consumed by downstream classification and regression models.• Built scalable distributed data processing frameworks using Amazon EMR (including Serverless EMR) to process multi-terabyte datasets; tuned Spark executor configs, partition strategies, and shuffle optimizations to cut job runtimes by 35%.• Converted legacy SAS data transformation logic into optimized SQL and PySpark workflows in Amazon Athena and EMR, replacing brittle SAS macros with testable, version-controlled PySpark and SQL transformations integrated into CI/CD pipelines.• Developed and orchestrated end-to-end ETL pipelines using AWS Glue 4.0, EMR 7.0, Lambda, S3, and Athena, reducing data processing time by 50% and improving pipeline reliability and data accuracy by 25%.• Built and maintained ML pipelines using Amazon SageMaker, supporting model training, hyperparameter tuning, model deployment, and real-time inference using managed endpoints.• Engineered feature stores and ML-ready data marts in Amazon Redshift and AWS Data Lake, supporting both scheduled batch scoring and low-latency real-time inference via SageMaker managed endpoints.

Education

• University of Missouri-Kansas City

  Master's degree, Computer Science