Al-Mamun Sarkar

Engineered a full-stack web application using Django for the backend and React for the frontend, powering a scalable lead generation system that enhanced data accessibility for marketing.

Optimized data processing workflows with Pandas and managed high-volume background tasks using Celery, significantly enhancing system efficiency and throughput for large datasets.

Implemented advanced web scraping solutions using Scrapy, increasing the volume and quality of contact and job information extracted from diverse online sources by an estimated 30%.

Designed and maintained robust data infrastructure utilizing GCS for storage, MySQL for relational data, Redis for high-speed caching, and BigQuery for analytical data warehousing, ensuring data integrity and rapid access for analytics.

Integrated RabbitMQ for efficient message queuing, enabling seamless communication and processing across distributed system components and improving system reliability.

Designed and implemented high-performance RESTful APIs using PHP, Laravel, and Flask, integrating Redis for caching to support dynamic Single Page Applications (SPAs) built with JavaScript and Vue.js.

Developed and deployed auto-scaling Golang Microservices on Google Cloud Run (Serverless), enhancing system elasticity and optimizing resource utilization for peak loads.

Managed and analyzed large-scale data utilizing Google Cloud Platform services including Cloud Storage, Apache Beam, and Dataflow, optimizing data pipelines for BigQuery, MongoDB, and MySQL.

Configured and managed Nginx as a web server and load balancer, and leveraged Docker for containerization, improving application performance and ensuring robust, scalable deployment.

Optimized database performance by strategically utilizing MySQL for primary data, MongoDB for unstructured data, and BigQuery for data warehousing, enabling the platform to serve millions of requests efficiently.