• Around 4+ Years of experience as an AWS data engineer in designing, building, and maintaining data pipelines on the AWS platform.• Skilled in using various AWS services such as EC2, S3, Redshift, Glue, Athena, EMR, and Lambda to develop and manage big data solutions.• Proficient in programming languages like Python, Scala, and SQL for data processing and analysis leveraging them to develop sophisticated data engineering solutions• Utilized Amazon S3 as a scalable and cost-effective storage solution for hosting data lakes and storing raw and processed data• Leveraged Amazon EMR for distributed data processing and analysis, including running Apache Spark and Hadoop jobs, resulting in a 20% improvement in data processing speed • Orchestrated and managed complex workflows using AWS Step Functions to coordinate multiple AWS services, including Lambda functions, Glue jobs, and more reducing workflow execution time• Built real-time data streaming applications using Amazon Kinesis for ingesting and processing streaming data leading to reduction in time-to-insight• Implemented data governance and security policies using AWS Lake Formation to manage access controls and auditing for data lakes• Managed and optimized Amazon RDS databases, including MySQL, PostgreSQL, and others, for various applications and workloads, resulting in improvement of database performance• Designed and developed applications using Amazon DynamoDB for NoSQL database requirements, ensuring high availability and scalability.• Created and optimized queries in Amazon Athena for ad-hoc analysis and reporting on data stored in Amazon S3 resulting in 32% reduction in query execution time• Set up and configured CloudWatch for monitoring AWS resources and applications, creating custom dashboards, and setting up alarms for proactive issue detection• Worked in an agile environment and have good insight into agile methodologies and Lean working techniques. Participated in Agile ceremonies and Scrum Meetings
