SAURABH BHANGE

● Reduced data pipeline latency by 40% by designing a scalable, event‐driven architecture using AWS Glue, Lambda, Step Functions, and Amazon SQS for efficient data transformation. ● Developed robust ETL pipelines using Python and SQL to extract and process data from the Google Analytics API, and automated daily data loads in Apache Airflow with support for schema evolution, efficient partitioning strategies, and scalable data modeling. ● Improved System Reliability: Designed a proactive monitoring and alerting framework using AWS CloudWatch, New Relic, and PagerDuty, enabling real‐time observability, faster anomaly detection, and quicker incident response, resulting in improved system uptime and reduced operational downtime. ● Reduced Cloud Costs by 40%: Performed a thorough cost optimization analysis of the data lake architecture, making key changes to S3, Athena, and Glue jobs that resulted in a 40% reduction in data processing costs. ● Automated Infrastructure with IaC: Automated the creation of data pipeline resources using AWS CDK (Python) and deployed them through CI/CD Workflow. Maintained a central configuration file to enable code reusability, making it easy to add new instances of the same resource and quickly recreate infrastructure during AWS account migration.