In my role as Director of Technology, I spearheaded the development and management of a robust data extraction pipeline. Our primary goal was to ensure compatibility with major ecommerce platforms like eBay, Etsy, and Shopify. To achieve this, we integrated Rest APIs, oAuth2.0, cURL, and Beautiful Soup into a unified web application, simplifying data extraction from any ecommerce website. Deploying the pipeline on AWS cloud infrastructure using CloudFormation, Docker, ECS, RDS, EC2, and S3 ensured seamless execution and simplified issue debugging. Notable accomplishments included implementing data extraction from eBay, Etsy, and Shopify via oAuth2.0, utilizing Beautiful Soup for Charish and Chrono24, and creating customized solutions for VIP clients. Additionally, we developed an Image extractor for downloading and rehashing image URLs from S3, established an internal API layer for data parsing and request handling, and incorporated UX/UI features for extraction requests. Leveraging AWS Lambda functions, we automated business practices for large-scale data, managed extensive SQL databases with dynamic schemas, and implemented features like automated notifications and 2FA login for enhanced security.
During my role as Director of Technology, I tackled the challenge of identifying unavailable lots in the auction business due to virtual consignment. I led the development of an application to detect these lots in real-time from ecommerce platforms, preventing fulfillment issues. We detected over 6,500 lots daily, removed items from our platforms while archiving them, and conducted market research for vendor pitches. By automating the application and optimizing its performance, we reduced detection time by 175% and improved accuracy by 50%. These efforts led to a 100% decrease in order processing time, enhancing operational efficiency significantly.
As a Full-Stack Developer, I created an application capable of simulating a ventilator for multiple patients, accommodating up to four individuals under specific conditions. This involved processing various inputs through a machine learning algorithm and displaying results on an interactive chart. Key aspects of the project include incorporating over 30 dynamic UI elements for input submission, implementing pressure control, and developing each ventilator to handle individual patient inputs. A supervised machine learning algorithm, trained on a dataset of 15,000 ventilator records, ensured accurate data output. The user interface features customized range sliders and synchronized animations with user navigation. Back-end operations, such as data collection and analysis, were performed using PHP, while Chart.js facilitated responsive chart animations.
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C++
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Urdu
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English (Fluent)