Data Organization

Data is structured systematically, allowing users to locate and access information effortlessly, particularly in large datasets.

• Predefined input fields ensure the accuracy and consistency of information entered.
  
• Dropdown menus standardize terminology, preventing inconsistencies in user input.
  
• Consistent terminology enhances the ease of retrieving relevant information.
  

Data Integrity

Accuracy, consistency, and reliability are upheld through every update and transaction, ensuring trust in the data.

• Logical checks prevent manual errors, automating the validation process to maintain data quality and integrity across the system.

1. Needs Assessment and Integration

Led the planning phase by gathering and integrating requirements from various departments, ensuring that all stakeholders’ needs were aligned with the project's objectives.

2. Project Management and Consultancy

Managed the entire project lifecycle, from initial design to final deployment, while also providing expert consultancy to ensure that the technical and operational aspects were executed efficiently.

3. Frontend Construction Using JSON

Developed a well-structured JSON file that served as the primary source for frontend engineers to build the user interface. This approach defined all the rules and relationships between inputs, reducing the need for frequent communication while improving the future maintainability of the system.

4. Dockerization and AWS Architecture

Dockerized the application to enable seamless deployment on Amazon ECS via GitLab CI/CD pipelines. Additionally, designed and implemented the AWS architecture to support the application, ensuring scalability, reliability, and efficient cloud resource management.