# Shreya Nandhivargam > Software Engineer | Backend & Data Systems | Python, FastAPI, Django, Kafka, Spark | AWS, Docker, Kubernetes | Real-Time Payments & Analytics Location: San Francisco, California, United States Profile: https://flows.cv/shreyanandhivargam 🚀 Engineering platforms where payments, intelligence, and real-time data converge. At Stripe, I build the systems behind seamless global payments—where milliseconds matter. From scaling resilient APIs with FastAPI and Django REST to streaming billions of payment events with Kafka and Spark, I help power the financial core that enables businesses to move money securely and instantly. Across the stack, I work end-to-end: → Backend & APIs: Python, Node.js, Celery, RabbitMQ, FastAPI, Django REST → Data & Streaming: Kafka Streams, Spark, PySpark, Redis for real-time metrics like ARPU, success rate, and churn → Storage: PostgreSQL + MongoDB to balance structured payments data with flexible client metadata → Infrastructure: AWS (EKS, Lambda, S3, SQS), Docker, Helm, Terraform for scalability, automation, and observability → Front-end: React, TypeScript, Material UI for live dashboards that turn data into insight Before Stripe, I engineered large-scale ad engagement and optimization systems at Meta—building low-latency pipelines that processed millions of real-time events, deploying ML models for ad personalization and fraud detection, and boosting targeting accuracy by 20%. What drives me? 👉 Designing clean, event-driven systems that are observable, fault-tolerant, and built for change. I thrive in teams where engineers own problems end-to-end—from data pipelines to product APIs—and where system reliability, data integrity, and performance aren’t just goals but guarantees. ## Work Experience ### Software Engineer (Stripe PayFlow) @ Stripe Jan 2024 – Present | United States • Managed live payment workflows for enterprise clients, processing multi-currency transactions, refunds, chargebacks, and scheduled payouts using Python (FastAPI, Django REST) on a daily basis. • Monitored Kafka payment event streams in real-time, reacting instantly to failed transactions, cancellations, and disputes, improving event reliability to 98% and reducing processing delays by 20%. • Automated retry logic and background jobs with Celery and RabbitMQ, ensuring payment retries, client notifications, and financial operations ran smoothly, increasing retry success by 15%. • Built real-time analytics pipelines with Spark, PySpark, Kafka Streams, and Redis, generating live KPIs like transaction success rates, revenue trends, ARPU, and payment efficiency, helping teams take immediate action. • Managed PostgreSQL for transactional data and MongoDB for dynamic client metadata, updating rules and discount configurations in response to promotions or changing business requirements. • Designed and maintained RESTful and GraphQL APIs (FastAPI, Ariadne) for internal dashboards, operational tools, and client portals, providing live transaction and invoice data to support finance and operations. • Created interactive dashboards using React.js, TypeScript, Chart.js, and Material UI to visualize failed payments, refunds, revenue alerts, and transaction trends, allowing operations teams to detect and resolve issues in real-time. • Developed microservices and backend tools in Python and Node.js for event validation, financial exports, and automated reconciliation, improving day-to-day operational efficiency. • Packaged services with Docker, deployed on AWS EKS using Helm charts and blue-green deployments, while managing serverless workflows with AWS Lambda, SQS, S3, and CloudWatch for continuous monitoring and backups. ### Software Engineer @ Meta Jan 2020 – Jan 2023 | India • Developed a Python-based backend platform using Flask and FastAPI to handle millions of ad impressions and user interactions daily, delivering real-time engagement insights, content recommendations, and ad optimization metrics for Meta. • Built modular microservices for user segmentation, ad scoring, personalized recommendations, and revenue tracking, exposing secure REST APIs for internal analytics and ad operations teams. • Implemented low-latency, event-driven pipelines with Apache Kafka to stream user clicks, ad impressions, session events, and engagement metrics in real-time, powering instant predictions and content ranking. • Created interactive dashboards with React.js, TypeScript, Redux, HTML5, and CSS3 to visualize live engagement trends, revenue anomalies, and priority alerts, enabling faster decision-making for ad optimization and campaign adjustments. • Developed machine learning models using scikit-learn, TensorFlow, and Pandas for real-time engagement prediction, churn analysis, and personalized content ranking, improving ad targeting accuracy by 20% and user retention by 15%. • Managed data storage and retrieval with PostgreSQL for structured data, MongoDB for unstructured logs, and Redis for caching highdemand predictions, reducing dashboard load times by 35%. • Built asynchronous ML inference pipelines using FastAPI and asyncio to handle thousands of prediction requests per second, ensuring reliability during peak traffic. • Developed anomaly and fraud detection services to identify unusual engagement patterns, click spamming, and bot activity, reducing false positives by 25% and strengthening revenue integrity. • Deployed services on AWS (EC2, S3, Lambda, RDS), containerized with Docker, and orchestrated using Kubernetes (EKS) for high availability, auto-scaling production workloads. • Automated CI/CD pipelines using GitHub Actions, Helm, and Terraform for continuous integration and secure deployments across development. ## Education ### Master's degree in Information Technology Saint Louis University Jan 2023 – Jan 2025 ## Contact & Social - LinkedIn: https://linkedin.com/in/shreya244 --- Source: https://flows.cv/shreyanandhivargam JSON Resume: https://flows.cv/shreyanandhivargam/resume.json Last updated: 2026-03-22