Abdulmalik Raji

Retail Sales and Inventory Management Application.

• Collaborated with cross-functional teams, including frontend developers, product managers, and others, to deliver core backend features for Trendpos, a comprehensive sales and inventory management system, using Go, GORM, and PostgreSQL.
• Developed essential modules, including sales processing, regional reporting, and stock management, handling over 1,000 daily transactions, ensuring robust performance and reliability.
• Incorporated Keycloak to manage secure authentication and authorization, streamlining user management for over 500 accounts and reducing authentication-related errors by 30%.
• Implemented advanced sales reports with filtering options, empowering businesses with detailed analytics, and improving decision-making efficiency by 20%.
• Optimized and containerized services using Docker, enhancing scalability and reducing deployment times by 50% through a CI/CD pipeline with Azure DevOps, and Azure Container Registry.
• Worked in an Agile environment, participating in daily stand-ups and sprint planning, ensuring timely delivery of features, and continuous iteration based on stakeholder feedback.

Full-stack real-time sales tracking dashboard (TypeScript, Next.js, Nest.js, Azure Cosmos DB).

• Built both the frontend and backend to handle up to 10,000 sales transactions per second, with almost no latency, enabling real-time processing of live sales and geographical data.
• Implemented WebSocket connections for live updates, improving user interaction, and reducing data latency by 30%.
• Designed data visualization features, including sales heatmaps and category-based filters, offering users the ability to drill down and analyze large volumes of transaction data.

Product Barcode Matching System.

• Custom Python API, utilizing advanced text-matching algorithms to accurately match and validate missing product codes in datasets consisting of over 70,000 entries.
• Streamlined product management processes by reducing data entry errors by 50% and improving system accuracy by 40% through the utilization of the specialized API.
• Integrated with the product database, automating product code assignment using web scraping data, increases product data accuracy by 35%.

Web Scraping projects

• Led various web scraping projects aimed at extracting product data from various websites, with varying structures and layouts.
• Utilized Pandas to process and store large datasets, improving data management efficiency by 30%.
• Applied design patterns and SOLID principles to optimize the application, reducing runtime errors by 40%, and enabling seamless daily execution.
• Prepared detailed technical documentation for future reference and maintenance.