Martin Barbason

At Toyota, I worked on a digital twin system simulating forklift behavior and its interactions with real-world components such as sensors, screens, and control panels. The goal was to create a high-fidelity simulation that could mirror actual forklift operations for testing and validation purposes.

I developed Python-based microservices that handled simulation logic and communication with physical hardware. We used Redis for fast in-memory data exchange to ensure low-latency synchronization between the back end and real-world devices.

The system was deployed on Azure, where we leveraged cloud compute services and CI/CD pipelines to manage updates and scalability. We followed test-driven development using pytest to ensure robustness and maintainability across all services.

This project showcased my ability to build real-time, hardware-integrated back-end systems with cloud infrastructure and event-driven components, ensuring accurate and efficient simulations in an industrial environment.

At TFTC, I worked on a real-time data integration and tracking platform for the largest agricultural food retailer in the U.S. The platform consolidated and monitored live data from numerous systems to ensure product traceability, inventory accuracy, and operational visibility across the supply chain.

We built an event-driven architecture using Kafka Connect to stream and process data from both internal services and third-party providers. I developed core Python back-end services and exposed the data through efficient, flexible GraphQL APIs using Graphene, enabling real-time access by multiple front-end applications.

To support low-latency responses and reduce load on primary databases, we integrated Redis for caching and transient data handling. Our system scaled to handle high-throughput data volumes while maintaining responsiveness and reliability. I followed test-driven development practices to ensure system correctness and stability.

This project showcased my ability to build robust, scalable back-end solutions for critical real-time tracking applications in the retail and logistics sector.

At Siemens, I worked as a full-stack developer on a greenfield project to build a real-time monitoring and predictive maintenance platform for CNC machines. Our team of 10 engineers designed the system using Python microservices to process large volumes of industrial data collected from sensors across the production line. The goal was to extract KPIs and detect early signs of malfunction to schedule preventive maintenance and avoid costly downtime.

The architecture followed event-driven principles, with microservices communicating asynchronously in response to anomalies and threshold breaches. I developed core back-end services, handled data normalization and processing, and contributed to live dashboards used by operators and engineers.

We used Redis for caching and low-latency messaging, and deployed the system via Docker and Kubernetes to ensure scalability and resilience. I also implemented unit and integration tests using pytest to maintain high code quality throughout the project lifecycle.

Easypharm is a Belgian mobile application developed via Flutter, allowing patients to contact their favorite pharmacy easily. It is composed of a chat with the pharmacist, a shop page allowing the patient to order drugs with or without a medical prescription, navigation to your favorite pharmacy, access to articles posted by the pharmacist, and promotions on drugs made by the pharmacist.

I developed the front end of the application using Flutter based on a design sprint conducted with the client. I was also the technical lead managing the back end and infrastructure team. My role was to design the project's infrastructure and guide the team through the solution. The back-end structure used AWS Lightsail, SQL database, GraphQL API, automated CI/CD, and Firebase mobile notification services.

I developed a mobile and web application tool to help managers of a Luxembourg fintech company to manage their $50 billion cumulated ethical funds. To do so, the tool needed to analyze real-time data from the funds to flag and notify managers when it needed their attention.

The application was divided into two: first, a web data analysis application created in Ruby on Rails allowing managers to connect their funds with real-time data feeds given by external sources and analyze associated KPIs. The second was a mobile application notifying the investment manager of any abnormal or pertinent status of the fund, helping them micro-managing each fund when needed.

Because each error or bug from the web/mobile application could result in huge losses, the test coverage of the apps was essential, and test-driven development was used to increase this confidence in my work.

Designed and developed internal mobile and web applications for industrial companies, such as SABBCA, Airbus, and FN Herstal. The mobile applications were done in Flutter and web applications in Node.js. The company's management uses the dashboards to control KPI over their CNC machines.