Aws Cloud Data Engineer
Current- Directed a cross-functional team in the full migration project at Johnson & Johnson, overseeing the seamless transition of data infrastructure and applications.
- Spearheaded the development of data processing pipelines utilizing Spark and Flink technologies, achieving a remarkable 30% reduction in processing time for real-time analytics of health cases data.
- Implemented RESTful APIs to facilitate integration with internal CRM systems, resulting in a notable 20% decrease in data synchronization errors. Adopted GraphQL to enhance data querying capabilities and facilitate.
- Orchestrated the deployment of containerized microservices using Docker and Kubernetes, streamlining deployment processes and reducing deployment time by 40%. This initiative significantly improved system scalability.
- Integrated Kenvue's proprietary genAI solution into the data processing pipelines, leveraging advanced machine learning algorithms to enhance data analysis and decision-making processes.
- Employed optimization techniques to enhance PostgreSQL database queries, yielding a substantial 25% improvement in query performance tailored specifically for managing health cases data. Leveraged SQL expertise for.