I was part of the core team that developed a highly scalable distributed surveillance application for a state law enforcement agency, designed to extract data from dark web sites and store it in a graph database for post-processing. Utilizing a comprehensive technology stack—including Python, Strawberry, Beanie, MongoDB, RabbitMQ, and GraphQL—I helped architect a solution that enhances operational efficiency and data accuracy.
By integrating headless Chromium, we implemented a cutting-edge feature to capture precise screenshots of websites, seamlessly connected to an efficient work queue system for optimal performance. The queue management service, powered by RabbitMQ, enabled robust asynchronous communication, significantly enhancing system scalability and facilitating seamless task coordination within a distributed environment.
I also contributed to the development of health check endpoints to monitor the status of the queue management service, ensuring optimal functionality and verifying the health of all associated queues. Notably, I helped increase our website crawling capacity from one URL every minute to an impressive 60 URLs per minute. My contributions facilitated near real-time monitoring of queue health.
This experience highlights my expertise in building scalable systems and my ability to collaborate effectively within a dynamic team, showcasing qualities that are essential for success in startup environments.