At Stripe, I engineer and scale backend services and microservices using Python (FastAPI, Django REST Framework) and Node.js to support global financial reporting, risk scoring, and anomaly detection. I design and optimize RESTful and GraphQL APIs, integrate Kafka event streams with Python consumers, and build real-time analytics pipelines with Apache Spark, PySpark, Kafka Streams, and Redis, improving reporting speed and reliability.

I manage PostgreSQL for structured reporting data and MongoDB for flexible metadata, while developing interactive dashboards in React.js, TypeScript, Chart.js, and Material UI to deliver live insights into anomalies, exposure metrics, and compliance trends. On the infrastructure side, I package and deploy services with Docker on AWS (EKS, Lambda, SQS, S3, CloudWatch), automate deployments with Terraform and GitLab CI/CD, and enhance reliability with Celery and RabbitMQ for job scheduling and retries.

My role also involves implementing secure authentication and role-based access control (OAuth2, JWT), building backend tooling for data validation and reconciliation, and maintaining comprehensive test suites with Pytest, unittest.mock, and Selenium to ensure accuracy of pipelines, financial calculations, and workflows.

At Meta, I designed and deployed large-scale content moderation systems that ensured safe and compliant experiences across Facebook and Instagram. I built and optimized Flask and FastAPI REST APIs to enable real-time classification, flagging, and routing of posts, while developing React and TypeScript dashboards for moderation teams to review flagged content, audit actions, and monitor compliance.

I engineered high-throughput asynchronous pipelines with Kafka, Celery, and Redis, reducing response latency by 28% and supporting near real-time moderation. I also designed PostgreSQL and MongoDB schemas for moderation logs and audit trails, ensuring GDPR and CCPA compliance with high query efficiency. To accelerate data workflows, I automated pipelines with PySpark and Airflow, cutting preparation time by 40%.

On the infrastructure side, I leveraged AWS (S3, Redshift, DynamoDB) for scalable storage and analytics, containerized services with Docker, and orchestrated deployments on Kubernetes with automated CI/CD pipelines via Jenkins and GitHub Actions, achieving 99.95% uptime. I strengthened system security with OAuth2, JWT, and RBAC, and improved reliability through observability and monitoring using Prometheus, Grafana, ELK, and OpenTelemetry.

Additionally, I enhanced API resilience with unit and integration testing (Pytest, unittest), and mentored junior engineers on Python microservices, React, Kubernetes, and AWS best practices, boosting onboarding efficiency and accelerating project delivery.