Ram Kishore S

Develop real-time streaming pipeline processing 1 TB of transactions daily using Apache Kafka, GCP Dataflow, Pub/Sub, Spanner and BigQuery, ensuring high availability and fault tolerance. Optimize SQL scripts to reduce runtime by over 25% for daily schedules with the help of GitHub Workflow. Oversee Spanner Database maintenance, establishing and evolution of standard Schema for consumers. Architecting an automated ETL framework with Python in parallel to main pipeline, reducing efforts by 30%.

Implement data validation techniques to ensure the integrity of transformed data throughout the ETL pipeline. Achieve 99.5% accuracy in data migration, ensuring high-quality data integrity and compliance with industry standards. Create solutions for database linking and quality report generation, enhancing data accessibility and insights across multiple departments. Drive machine learning initiative to reduce manual error rate by 20%, resulting in improved efficiency and accuracy across the data team. Automated an entire task leveraging Selenium package in Python to reduce manual efforts by over 70%.

Led a team of 3 to design an ETL pipeline to reduce processing time by 20% through Informatica mappings, mapplets, workflows and Autosys (Scheduler), resulting in improved data processing efficiency. Collaborated with Technical Architects and Delivery Managers on data initiatives and creating Machine Learning POCs to enhance project outcomes and ensure alignment with business objectives. Mentored and trained team in Python and Machine Learning skills to groom them to participate in multiple Hackathons, creating a culture of continuous learning within the team.