Nayan Bhiwapurkar

Data Engineering Intern

HyperData•  June 2024 - August 2024•  Tempe, AZ, US

Optimized cloud infrastructure for API scalability and reliability using AWS, Terraform, Docker, and Airbyte for data synchronization. Enhanced data processing pipelines for better efficiency and throughput by leveraging Python and PostgreSQL to handle larger datasets. Developed and refined SQL scripts to advance data transformation tasks and improve the API’s functionality for diverse use cases. Executed API performance testing, benchmarking, and created documentation with usage examples.

Data Engineer

Cognizant Technology Solutions•  November 2020 - June 2023•  Pune, MH, India

Led a retail data engineering project, processing daily data volumes of about 100GB using Apache Spark, Python, SQL, and Amazon S3. Designed structured data models, including dimension and fact tables, to provide valuable context for point-of-sale analysis. Implemented an incentive program based on sales performance, boosting motivation among sales teams. Designed customer retention strategies using targeted coupons, increasing engagement with infrequent buyers. Enhanced data pipeline performance by applying Spark optimizations like caching and broadcast joins. Automated workflows with Airflow, CRON jobs, and integrated Azure CI/CD pipelines. Pioneered real-time monitoring services for a Covid Vaccine Survey System using Java, SpringBoot, Apache Kafka, Splunk, and Dynatrace, reducing system response time by 20% and improving user experience by 15%. Engineered data ingestion pipelines with Apache Kafka, optimizing data flow and storage efficiency by 25%. Spearheaded the migration from a SOAP-based service to a scalable RESTful microservices architecture, improving code quality by 30%. Documented APIs, wrote JUnit tests with Mockito, and debugged critical issues to boost system reliability and developer productivity.

Software Engineering Intern

Cognizant Technology Solutions•  December 2019 - April 2020•  Pune, MH, India

Developed a high-performing, Twitter-like social media platform leveraging Java, Spring Boot, Angular, REST APIs, and Microservices. Enhanced scalability with Kafka MQ and MongoDB, and implemented real-time monitoring with Grafana, Zipkin, and Prometheus. Automated deployment with AWS Code Pipeline and Docker Build, improving efficiency by 40% and delivering a robust CI/CD pipeline.