Proficient Software Engineer with 6 years of expertise in designing and developing cloud-native software applications.
WARNING: This portfolio website is designed by a backend developer (me).
I am a Software Engineer with 6 years of hands-on experience, I specialize in building cloud-native, data-intensive, and scalable applications using Java, Spring Boot, Python, AWS, Kubernetes, Kafka, Gitlab, and Microservices. Currently exploring Data Analytics Domain. Additionally, my expertise in software engineering and Cloud Computing makes me confident in my ability to contribute significantly to any enterprise project.
Engineered a scalable microservice application to aggregate financial data, catering to a user base exceeding 5 million.
Developed a cutting-edge application facilitating the aggregation of 3000 Fiserv-backed Financial Institutions.
Spearheaded and executed flawless internal platform and cloud migrations, achieving seamless transitions.
Achieved 90% efficiency in CI/CD by containerization of microservices and orchestrated deployment on a Kubernetes.
Innovated by crafting a bespoke Maven Enforcer Plugin that eradicated 100% of human errors leading to potential diamond dependency conflict.
Developed a RESTful application with precision, enabling the seamless aggregation of debt financial data for 20,000 global institutions.
Architected AWS infrastructure, resulting in reduction of infra cost by 20% while ensuring the reliability and scalability of the systems.
Automated Staging and Production deployments and rollbacks, resulting in a 50% reduction in deployment cycle time.
Implemented a Kafka monitoring pipeline, reducing critical incidents by 25% and enhancing overall reliability.
Created a Rest API Test Automation Tool, resulting in savings of 1000’s of dollars by avoiding the need to purchase Soap-UI licenses.
Crafted an Excel-driven UI Automation Testing Tool, resulting in a notable 60% reduction in UI Automation testing duration.
Initiated containerization and container orchestration of applications, resulting in a 30% reduction in deployment time and a 20% increase in scalability.
