Prantik Goswami

• Built scalable AWS & Azure data pipelines with Airflow, Athena & Databricks. • Integrated GenAI & Text2SQL models enabling natural language data queries. • Created dashboards in Django Dash & Grafana for business insights. • Automated Google & Apple Ads API reports to track engagement & retention. • Deployed Dockerized FastAPI microservices for cross-team data sharing. • Cut query cost by 40% with SQL optimization & achieved 99.9% uptime.

• Built scalable AWS data pipelines processing 2M+ daily events. • Automated workflows with Airflow and Athena for real-time analytics. • Optimized SQL queries, reducing cost by 40% and execution time. • Ensured GDPR-compliant data anonymization and security. • Built Django Dash dashboards improving KPI coverage by 30%. • Managed AWS DevOps achieving 99.9% uptime and 20% cost savings.

• Developed scalable microservices using Java, Spring Boot, and Quarkus to deliver robust service APIs aligned with data-driven solutions and GDPR compliance. • Implemented reliable, automated data pipelines in Jenkins, ensuring seamless data flow. • Maintenance work of the existing application using bug fixes and code quality improvements. • Replacing old codes with newer coding styles to improve readability and maintainability. • Writing unit and integration testing for higher code coverage.

• Designed and developed scalable microservices with NestJS (Node.js) and Spring Boot (Java) for resilient API layers; managed data in PostgreSQL using pgMigration for robust schema versioning. • Implemented JWT authentication and dynamic Angular components. • Led successful migration from legacy to microservice-based CRM, building custom REST APIs in Groovy/Java for seamless data transfer. • Deployed and orchestrated containerized applications using Docker and Kubernetes for efficient, scalable operations.

• Migrating old legacy monolithic back-end system to newer micro-service architecture. Used spring boot for the micro-services design. The micro-services exposed REST APIs to be consumed. • Message broker service of the RabbitMQ implementation for the request handling of the microservices. • Used the Spring-data-JPA for the Back-End database integration. • Development of UI screen components(Inc. Common) using Angular 2.0. The front-end design followed MVC architecture for the separation of concerns.

• Development and configuring a Java Spring-based product called Exigen. The task included the creation of client-specific components in the product and maintaining the life cycle of the components. • The product used Spring MVC architecture. The back-end used hibernate to connect to the Oracle database. • The front-end used HTML5 , Primefaces and Richfaces JSF implementation for the product Life Engage.

Done my master's in Web and Data Science. My major subjects include Machine Learning, Web Information Retrieval, Data Science, and Web Engineering. My achievements include - 1) Combining NLP and Deep Learning for automatic extraction of metadata from German academic papers. (Group Project, the paper published in JCDL 2021) 2) My master's thesis was developing an NLP-based Deep Learning application for the e-Healthcare diagnosis.