Utrecht, Veenendaal

Work Hours
Monday to Friday: 9am – 5pm
Weekend: 10am – 5pm


As the leader of the Qfact Analysis Service project, I oversaw the entire project lifecycle, from the initial functional design to the successful release. This project was initiated to address the declining performance of existing analysis methods as data volumes increased, necessitating a more robust and scalable solution.

Project Description

The primary goal of the Qfact Analysis Service project was to enhance the performance and scalability of our analysis methods. As data volumes grew, existing methods struggled to keep up, leading to slower performance and inefficiencies. Collaborating closely with the software architect and a senior developer, I played a crucial role in designing the overall architecture and detailed software modules.

We implemented the project within a new service integrated into our existing microservices architecture. A key component of the solution was the use of a MySQL database to ensure frequent and reliable data synchronization.

Challenges Faced

Several challenges were encountered during the project:

  • Performance Issues: Existing analysis methods could not handle the increasing data volumes efficiently.
  • Complex Data Management: Managing and analyzing complex data spread across multiple microservices.
  • User Interface: Creating a user-friendly interface for configuring analysis reports.

These challenges were addressed through innovative architectural design, strategic use of MySQL, and effective collaboration with key team members.

Technologies and Tools Used

The project utilized the following technologies and tools:

  • Microservices Architecture: Ensuring the new service integrated smoothly with existing components.
  • MySQL Database: For efficient data synchronization and storage of analysis reports.
  • Cronjobs: To dynamically create complex MySQL queries for report generation.

These tools were selected for their robustness, scalability, and compatibility with our existing infrastructure.

Key Features or Achievements

The project’s key features and achievements include:

  • User-Friendly Interface: Developed an intuitive interface for configuring analysis reports.
  • Dynamic Report Generation: Implemented cronjobs to create complex MySQL queries dynamically.
  • Specialized MySQL Reports Tables: Maintained specially created tables to ensure rapid report loading and efficient data handling.

Results and Outcomes

The Qfact Analysis Service project yielded significant results:

  • Improved Performance: Enhanced the performance and scalability of analysis methods, even with increasing data volumes.
  • Efficient Data Handling: Successfully managed and analyzed complex data across multiple microservices.
  • Positive User Feedback: Received positive feedback for the user-friendly interface and rapid report loading.

Lessons Learned

Key lessons learned from the project include:

  • Scalable Design: The importance of designing scalable solutions to accommodate future data growth.
  • Collaboration: Effective collaboration with key team members is essential for successful project delivery.
  • User-Centric Approach: Prioritizing user needs and creating intuitive interfaces significantly enhances user satisfaction.


Leading the Qfact Analysis Service project was a highly rewarding experience, showcasing my ability to design and implement scalable, efficient, and user-friendly solutions. The project’s success demonstrates my expertise in architecture design, data management, and collaborative problem-solving. If you are interested in learning more about this project or discussing similar opportunities, please feel free to contact me.

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