# Divya Y > Senior Software Engineer | Backend & AI-Enabled Systems | Python, Node.js | Distributed Systems, Microservices | APIs | AWS | Stripe & Amazon Location: San Francisco, California, United States Profile: https://flows.cv/divyay 🚀 Software Engineer (5+ years) building and scaling backend platforms, distributed microservices, and real-time data systems across fintech and large-scale enterprise environments. 💡 Designed and optimized high-throughput, event-driven systems processing millions of transactions and events per day, enabling real-time fraud detection, risk analytics, and automated decisioning. 🧠 Specialized in ML-driven backend systems, integrating machine-learning inference into production workflows to support fraud scoring, anomaly detection, and data-driven intelligence at scale. ⚙️ Strong expertise in Python, FastAPI, Django REST Framework, Node.js, SQL, and RESTful APIs, building secure and scalable services used by global internal and customer-facing platforms. ☁️ Hands-on experience with AWS cloud infrastructure, leveraging Docker, Kubernetes (EKS/ECS), Terraform, and CI/CD pipelines to deploy reliable, observable, and cost-efficient cloud-native systems. 📈 At Stripe, built backend services and real-time APIs for fraud scoring, dispute automation, and risk workflows, improving detection accuracy, reducing latency, and supporting global payment traffic. 🛒 Previously at Amazon, engineered distributed microservices, data pipelines, and big-data processing systems using Kafka, Spark, Airflow, and AWS, processing millions of events daily with high availability and strong SLAs. 🔒 Experienced in building secure microservices with OAuth2, RBAC, and compliance-focused architectures, ensuring data protection, auditability, and enterprise-grade reliability. 🌍 Passionate about backend engineering, scalable systems, and real-world problem solving — I enjoy turning complex data and infrastructure challenges into resilient, high-impact platforms. ## Work Experience ### Software Engineer @ Stripe Jan 2024 – Present • Engineered backend services using Python, Django REST Framework, and Node.js to analyze transactions, detect anomalies, and automate fraud scoring for global payment traffic. • Built RESTful and WebSocket APIs enabling real-time fraud alerts, dispute notifications, and transaction review workflows for risk analysts. • Integrated Stripe Radar, Experian, and LexisNexis APIs to enhance fraud models with identity verification, device fingerprinting, and behavioral analytics. • Integrated ML-based fraud-scoring models into backend pipelines, enabling real-time risk prediction and improving high-risk detection accuracy by 14%. • Developed asynchronous microservices using Celery, RabbitMQ, and Kafka Streams, reducing fraud-analysis latency by 35%. • Built data pipelines for feature extraction and ML inference using Python, Kafka Streams, and PostgreSQL to support continuous model improvements. • Implemented automated dispute and chargeback pipelines using PostgreSQL, Redis, and batch processing, improving dispute win rates by 18%. • Designed and enforced OAuth2, RBAC, and service-level access controls to secure customer and fraud-risk data across distributed systems. • Built internal dashboards with React.js, TypeScript, and Material UI to visualize fraud scores and trends, reducing investigation time by 30%. • Containerized services using Docker and deployed on AWS ECS/EKS, implementing CloudWatch monitoring, S3 log archival, and anomaly alerts. • Automated infrastructure with Terraform and optimized GitLab CI/CD pipelines for reliable multi-environment deployments. • Developed unit, integration, and performance tests using Pytest, unittest, and Postman, achieving 95%+ test coverage. • Collaborated with Risk Science, Compliance, SRE, and Data Engineering to optimize fraud pipelines, improving response times by 22%. • Implemented observability with Prometheus, Grafana, and ELK Stack to detect fraud spikes, API anomalies, and ML model drift. ### Software Engineer @ Amazon Jan 2020 – Jan 2023 • Designed, developed, and owned cloud-native microservices using Python, FastAPI, and Flask to support high-scale distributed systems for internal customer, seller, and operational platforms. • Architected and implemented event-driven systems using Apache Kafka, AWS Kinesis, and AWS SQS, processing 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, metrics, and analytical outputs. • Orchestrated batch and near-real-time workflows using Apache Airflow, implementing automated backfills, dependency management, failure recovery, and 99% SLA compliance. • Designed and optimized relational and NoSQL data models using PostgreSQL and MongoDB to support transactional and semi-structured workloads, improving query performance by 30–35%. • Developed RESTful APIs with proper versioning, authentication, and authorization, following secure API design and AWS IAM best practices. • Built internal web applications and dashboards using React and TypeScript to enable monitoring of system health, data pipelines, and business KPIs. • Integrated machine learning inference services into backend APIs to support personalization, optimization, and anomaly detection use cases. • Collaborated with data science and ML engineering teams to productionize machine learning models, focusing on feature pipelines, scalable inference, and performance monitoring. • Implemented AWS data lake architecture using Amazon S3, enabling analytics and reporting through Amazon Athena and Amazon Redshift over multi-terabyte historical datasets. • Containerized services using Docker and deployed them on Amazon EKS (Kubernetes), implementing auto-scaling, rolling deployments, and resilient system design, reducing deployment-related incidents by 25%. ## Education ### Master's degree Sacred Heart University ## Contact & Social - LinkedIn: https://linkedin.com/in/divya-y-4ab439372 --- Source: https://flows.cv/divyay JSON Resume: https://flows.cv/divyay/resume.json Last updated: 2026-03-22