Ishani Pandya

Background

About Ishani Pandya

A results-driven AWS Data Engineer with around 5+ years of hands-on experience in designing, implementing, and optimizing data solutions on the Amazon Web Services (AWS) platformSuccessfully designed and implemented data warehousing solutions on Amazon Redshift, resulting improvement in query performance and reduction in operational costsProficient in utilizing AWS Glue for building robust ETL (Extract, Transform, Load) pipelines, automating data preparation, and optimizing data integration processes, contributing to efficient data-driven decision-making and analyticsSkilled in AWS Lambda for developing serverless applications and event-driven architectures, leveraging its scalability, cost-effectiveness, and seamless integration with AWS services to drive business agility and enhance operational efficiencyOrchestrated and managed AWS Elastic MapReduce (EMR) for big data processing, cluster management, and optimization, harnessing EMR's scalability and cost-efficiency to enable high-performance analytics and insights from large-scale datasetsLeveraged Amazon Athena for ad-hoc query analysis and its serverless query capabilities to enable a structured querying of data stored in Amazon S3, accelerating data analysis and reporting without the need for complex infrastructure managementAutomated infrastructure provisioning and management using AWS CloudFormation templates, reducing deployment time and implemented comprehensive monitoring and alerting solutions with AWS CloudWatch, reducing downtime through proactive issue detectionCapable of designing and orchestrating complex data pipelines using AWS Data Pipeline, ensuring data flows efficiently and reliably and integrated Apache Kafka for real-time data streaming, achieving near-zero data loss and ensuring data consistencyUtilized Apache Spark for large-scale data processing, resulting in a 30% improvement in data processing efficiencyProficient in designing and optimizing SQL and NoSQL databases for various use cases, including performance tuning and data ModelingMaintained Hadoop clusters for batch data processing, optimizing job execution times by 40% and collaborated effectively with cross-functional teams using Jira and Confluence, ensuring clear project communication and documentationDemonstrated ability to troubleshoot and resolve complex data engineering issues in a timely mannerCollaborative team member with effective communication skills, capable of working in cross-functional teams to achieve project goals