Sai kiran ch User

Engineered and automated end-to-end ETL pipelines using Apache Airflow and Spark/Scala on AWS EMR, ingesting data from S3, performing large-scale transformations, and propagating processed data across S3, HBase, Kafka, and MySQL. Integrated LLM-based data transformation using LiteLLM into ETL workflows, assessing model outputs for accuracy and reliability and applying LLMs to standardize unstructu

Developed a React-based self-service reporting platform, enabling Time and Attendance users to independently create reports, eliminating 60% of dependency on technical teams. Integrated data extraction from AWS Glue, Redshift, and internal data lakes, optimizing data flow and accelerating report generation, cutting retrieval time by 35%. Revamped reporting processes, driving a 50% reduction in man

Designed and built a robust AWS-based data pipeline to integrate data from third-party vendor's SFTP systems into the internal data lake, increasing data processing efficiency by 75%. Spearheaded automation of database resource management for Redshift by developing a CI/CD pipeline, reducing deployment time by 80% and operational effort by 25%.

Engineered and implemented a robust Data Reconciliation Framework integrating Amazon S3 and Redshift to ensure data consistency and eliminate discrepancies during ETL processes. This framework resulted in a 42% reduction in data integrity issues, safeguarding against potential data loss and improving data reliability.

Grade: 8.39

Bachelor of Engineering with a GPA of 8.39. Coursework included Data Structures and Algorithms, Database Management Systems, Computer Architecture, Operating Systems, Machine Learning, and Deep Learning.