• Modernized legacy application by transitioning from a monolithic to a microservices architecture using Docker and Kubernetes, resulting in a 20% reduction in development, maintenance, and server costs.
• Automated build and deployment processes with a Jenkins pipeline, reducing lead time from 8 hours to 1 hour and enhancing development velocity. Implemented GitOps principles for continuous integration and deployment.
• Enhanced data security by implementing a GDPR-compliant design to address plaintext storage of user-sensitive data.
• Improved API accessibility by implementing version-agnostic backward and forward compatibility for REST services, eliminating client-side version adaptation issues

• Implemented GDPR compliance for PayPal's large Hive data warehouse by developing a Pyspark job to mask specific primary keys. This job efficiently processes 2 million records in 30 minutes, despite Hive 1 .1's limitations.
• Streamlined data ingestion by building a robust data pipeline using Spark for processing, Hive for storage, and Kafka for real time intake, meeting client requirements.
• Enhanced data processing by creating a Java application that listens to Kafka topics, parses XML files, extracts data, and stores it in Hive tables, efficiently handling batches of 50,000 records.
• Developed a RESTful API for PayPal Honey Entity using Spring and Java, facilitating efficient data access and management.

• Transformed the learning module processing with a high-performance Kafka pipeline, converting a serial job system into a parallel Kafka-powered pipeline. This boosted throughput by 10x, processing 1 million records in 1 hour instead of 1 hour per job.
• Architected a data pipeline that integrates archived and current data into a cost-effective Big Data cluster, enabling efficient reporting. Reduced report generation time for 5 million records from 24 hours to 30 minutes.
• Developed new features and REST services for the Certification and Curriculum modules based on product requirements.

• Collaborated closely with the business and built a robust framework within Matrix that efficiently calculates valuations for a massive portfolio.
• Revamped Deutsche Bank's exposure assessment for 90,000+ listed trades: Implemented advanced back-testing methods resulting in 30% more accurate risk analysis. Utilized agile methodology to optimize back-testing runs, resulting in faster and more accurate results.
• Streamlined trade valuation process by 50%: Designed and implemented high-performance Matrix system for 90,000+ trades.
• Implemented optimizations using Java, Hazelcast, and multi-threading within a distributed system, resulting in a remarkable increase in trade data processing speed by 48x. This reduction in processing time from 48 hours to 4 not only saved time but also significantly reduced costs, highlighting the effectiveness of advanced data structures and algorithms.
• Wrote a Hadoop MapReduce tool to process 1 million trades in the HBase table in 10 minutes (67%-t

• Implemented microservices to streamline core functions: Developed REST APIs for Breeds, Judges, and Events, cutting data access time by 50%.
• Developed secure login services using Spring Security for LDAP, GIGYA, and database systems, ensuring user confidentiality and data integrity.
• Automated data synchronization, slashing manual effort by 80% and enhancing data consistency across Oracle and MySQL databases.

• Enhanced Messaging campaign delivery streamlined: Created automated data upload for daily updates of us.hsbc.com customer data, improved customer engagement by 30%.
• Streamlined campaign data retrieval, saving Content Management team 1 hour per day

• Implemented and customized an Account, Budget, Works, and Billing Monitoring System. Achieved 20% increase in client satisfaction. Custom Billing Module reduced errors by 30% and received commendation for exceeding client expectations.