Harsh Agrawal

• Architected and implemented a comprehensive and seamless Event Analytics Framework, optimised for capturing, processing, and analysing diverse events or data points originating from applications, systems, and user interactions. It empowers businesses with invaluable insights, facilitating informed decision-making and driving strategic advancements. Handles approx 15-20 Million events per day.
• Built a spark-based Data Ingestion Framework that streamlined the movement of data from RDS replica to Redshift, significantly reducing the workload of data engineers to a simple YAML config file update.
• Implemented Redshift Workload Management (WLM) and introduced a role-based access system, enabling enhanced performance, secure data access, and tailored permissions for analysts, finance professionals, and engineering teams.

Client: Uber Transit

• Continuous monitoring and validation of pipelines for improved data reliability, efficiency and quality.
• Designing DFD’s to model better and more efficient flow of data and creating python based ETL pipelines using Apache airflow.
• Responsible for the design, build, test and maintain scalable and stable applications to support distributed processing using the Hadoop Ecosystem.

Client: Uber Adtech

• Developed plugins for extraction of Ad data from a wide variety of data sources and identifying ways to improve data reliability and efficiency and maintaining data quality for all data ingestion pipelines.

• Developed an internal React WebAPI project for Checklist Management System Portal
• Tech Used: NodeJS, MySQL DB Server, Swagger API, Mocha.