Currently, I work as a Software Developer at Holoss, where I am developing a centralized LCA/LCC document management platform that enables the creation, collaborative filling, and validation of life cycle and cost assessments. The solution is built on a FastAPI (Python) backend with PostgreSQL to ensure performance and data integrity, and a vanilla HTML, CSS, and JavaScript frontend for an agile and accessible interface. Authentication is managed via JWT with OAuth2 and bcrypt, and sessions are kept per tab using sessionStorage, ensuring independence between windows.

The entire environment is orchestrated with Docker and Docker Compose, guaranteeing replicability and scalability from development to production. The platform supports three distinct profiles – Partner, Company, and Admin – with specific workflows for filling, validation, versioning, and document history. It includes a section-based comment system (LCA, LCC, and Image), real-time notifications, an analytical dashboard for administrators, and Excel export with all versions. The goal is to transform technical data into structured and traceable information, promoting informed decision-making in mobility, energy efficiency, and sustainability projects.

During my time at the Centro de Computação Gráfica (CCG/ZGDV), I contributed to the PACMOBINOV project, developing a smart traffic management system that enhances connectivity between connected and autonomous vehicles (CAVs) and vulnerable road users (VRUs). My main contribution was the implementation of advanced collision detection and prevention algorithms in Python, operating in real-time and synchronizing with pedestrians via a mobile application and with vehicles through an On-Board Unit (OBU), orchestrated by a Road-Side Unit (RSU) ensuring low-latency communication. This work resulted in two peer-reviewed publications: one at IEEE (V2P communication architecture) and another at Springer (Kalman Filter-based localization enhancement).

As a data analyst, I specialize in transforming raw data into actionable insights that support strategic decision-making. I leverage Power BI (DAX, Power Query, ETL) to develop interactive dashboards and dynamic reports, combined with Python libraries (Pandas, Matplotlib) for advanced data manipulation and exploratory analysis. I have extensive experience in cleaning, modeling, and visualizing large datasets, ensuring data integrity and consistency to facilitate communication with multidisciplinary teams and drive data-informed business strategies.

I developed an embedded indoor localization system for the TEXP@CT project (PPS7 – Traceability Framework), aimed at the textile industry. Using C++ firmware on ESP32 microcontrollers, the system performs multiple rounds of BLE scans to collect RSSI samples, enabling more accurate position triangulation. The solution includes Wi-Fi communication via REST APIs, an embedded web interface for remote configuration, and integration with a Blynk dashboard for real‑time monitoring. The architecture ensures industrial robustness through NTP synchronization, automatic reconnection, and application state control. This work resulted in a peer‑reviewed publication in ScienceDirect.

Finally, I created the MITI Demo, a full-stack web platform for real-time sensor monitoring, built with Flask (Python) on the backend and JavaScript/Leaflet on the frontend. The application enables complete sensor lifecycle management (CRUD operations) with location mapping, simulates periodic temperature and noise readings using Pandas for data persistence, and provides dynamic dashboards with paginated tables, historical charts, and real-time filters. The platform also integrates a data collector management system supporting MQTT, Kafka, and API ingestion, demonstrating my ability to build scalable end-to-end data visualization solutions for operational decision-making.