Divya Y

Engineered backend services using Python, Django REST Framework, and Node.js to analyze transaction patterns, detect anomalies, and automate fraud-scoring workflows for global payment traffic. Built RESTful and WebSocket APIs to support real-time fraud alerts, dispute notifications, and dashboards for internal risk analysts. Implemented asynchronous microservices with Celery, RabbitMQ, and Kafka Streams to process high-volume event streams, reducing fraud-analysis latency by 35%. Integrated ML-driven fraud-scoring models into backend workflows, enabling real-time risk prediction and improving high-risk transaction detection accuracy by 14%. Built data pipelines for feature extraction and model inference using Python, Kafka Streams, and PostgreSQL to support continuous improvement of fraud-detection models. Designed and enforced OAuth2, RBAC, and service-level access policies to secure data across distributed microservices. Built internal dashboards with React.js, TypeScript, and Materi

Designed cloud-native microservices using Python, FastAPI, and Flask to support high-scale distributed systems for internal customer, seller, and operational platforms. Architected event-driven systems using Apache Kafka, AWS Kinesis, and AWS SQS; processed millions of real-time events per day with low latency and high availability. Built distributed data processing pipelines using PySpark and Spark SQL on Amazon EMR to generate terabyte-scale feature datasets and analytics. Orchestrated batch and near-real-time workflows using Apache Airflow with automated backfills and dependency management, achieving 99% SLA compliance. Optimized relational and NoSQL data models (PostgreSQL, MongoDB) to support transactional workloads, improving query performance by 30–35%. Containerized services using Docker and deployed on Amazon EKS with auto-scaling and rolling deployments, reducing deployment-related incidents by 25%. Contributed to AWS data lake architecture using S3, Athena, and Redshift fo