Siva Charan G

Built end-to-end ETL pipelines using AWS Lambda, Glue, PySpark, and Snowflake Snowpipe, enabling seamless ingestion, transformation, and loading of large datasets. Developed SQL queries (joins, CTEs, window functions, aggregations) to process 1M+ records, improving query performance by 30%. Automated data workflows with Apache Airflow and Cron jobs, reducing manual processing by 60% and ensuring timely delivery of Business Value and Data Pack reports. Performed large-scale data cleaning and transformation using PySpark and improved Spark jobs through partitioning and performance tuning, cutting execution time by 30%. Integrated AWS services (S3, Lambda, Glue, Snowflake) to standardize reporting pipelines, automate exports to SFTP, AWS and Snowflake, reduce manual effort by 50%. Collaborated with stakeholders to translate business requirements into scalable data solutions, ensuring reliable, analytics-ready datasets. Orchestrated Azure Data Factory pipelines to streamline raw data ingestion, transform data using Apache Spark following the Medallion Architecture (Bronze → Silver → Gold) framework, and store the processed data in Delta Lake for efficient access and analytics. Mentored

Designed and streamlined ETL pipelines using Python, PySpark, Hive SQL, HDFS, and AWS, reducing manual effort by 60% and improving reporting efficiency by 25%. Developed and optimised data workflows for Business Value and Data Pack reports, ensuring standardised, timely delivery of insights to stakeholders.

Create and deliver Business Value Reports, including customized value reports tailored to client-specific needs, leading to a 10% improvement in business insights.