Leiria, Leiria, Portugal
I am currently working in Business Intelligence (Data Science, Data Engineering, and Data Analysis) at REATIA, a fintech startup, where I oversee technology strategy, budget management, project execution, and team leadership to align with business objectives. I have led the planning and implementation of infrastructure, software architecture, microservices, and data modeling. I introduced a project-based team segmentation approach and streamlined internal development processes (BPMN), integrating support management. I designed and implemented end-to-end data flows, transforming raw data into actionable insights through independent microservices and internal backoffice monitoring systems. I also manage customer feedback and support requests, transforming them into new features based on market needs. I helped plan financing projects and obtain ANI certification. Under my leadership, daily data processing increased from 50,000 to 1.5 million, data aggregations grew from 100 to 7,000 per day, and search performance improved from 30 seconds to 2 seconds. This scalable model was successfully expanded to four countries, while reducing overall costs by 90%. Previously, I worked at the IT Services Directorate of IPLeiria, where I developed a Decision Support System (ETL + Data Warehouse + Reporting) integrating multiple external and internal data sources. Additionally, I developed a prediction algorithm to identify students who were accepted into a course but were unlikely to enroll, enabling the university’s academic services to provide targeted support and reduce dropout rates. Simultaneously, I was invited to be a professor and coordinator of a professional internship in a work context. Academically, I hold a background in Computer Engineering and am currently pursuing a Master’s in Management, complemented by certifications in Training for Trainers (CCP), Entrepreneurship, and other soft and hard skills. I am also actively improving my English skills to achieve a C1 proficiency level. My career began with the founding of Codebehind, a startup that developed MAPO.PT, a C2C and digital marketing platform for agricultural products, as well as an agricultural indicators and macroeconomic analytics platform. The startup received several awards for innovation and entrepreneurship. I secured partnerships, represented the project at industry events, and pitched in entrepreneurship competitions and investment rounds. Certifications and recognitions can be found in the sections below. For more details visit: https://knowledgeio.tech/portfolio/#about
📝 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: This project is divided into multiple sub-projects, each enhancing Data Engineering, Data Science, and Business Intelligence to optimize real estate data processing and analytics. 🚀 1. Bots – Automated Data Extraction A custom-built, object-oriented web scraping framework that automates data extraction from diverse real estate sources, ensuring scalability and adaptability. 🚀 2. ETL / ELT Processes The non-standardized real estate market complicates data-driven decisions. The transformation layer normalizes, applies business logic, and derives variables to enhance analytics. 🚀 3. Match – Data Deduplication & Aggregation Merges multiple listings of the same property into a single, accurate record using deduplication and merging techniques, improving data integrity. 🚀 4. OLTP & OLAP Databases A high-performance hybrid database supporting pivoting, slicing, dicing, roll-up, and drill-down on 10M+ records in seconds, enabling advanced real estate market analytics. 🚀 5. Business Intelligence & Advanced Analytics 🔹 Location Algorithm – Standardizes property locations, dynamically adapting to geographical and political updates, ensuring scalability. 🔹 Similarity Algorithm – Identifies comparable properties based on features, location, and business variables from historical and real-time market data. 🔹 NLP (Natural Language Processing) – Extracts detailed property attributes and insights from unstructured text data. 🔹 Computer Vision (CV) – Detects duplicate property listings by analyzing images to confirm they belong to the same asset. 🔹 RoomType Classification – Uses image recognition to identify and categorize rooms in property photos. 🔹 KPI's (Key Performance Indicators) – Generates basic and advanced performance metrics, leveraging aggregated data for business insights.
Digital Services Directorate (DSDi) - IPLeiria. 📝 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Development of a Decision Support System (DSS) for IPLeiria’s Administration, enabling full monitoring of the student admission process—from application to enrollment. For enrollment, collaboration with the Directorate of Academic Services (DSA). Initially designed for Bachelor's programs. The system was scaled to support TESP, Postgraduate, and Master's degrees. 🚀 Phase One – Data Integration & Business Intelligence: Before enrollment, the process is externally managed, with DGES providing non-standardized data (Word, PDF, Excel, MS Access, TXT). Admission rules change annually, requiring a dynamic extraction process. To address this, I developed a middleware layer that allows formula updates without impacting dependent processes. 🔹 System Integration – Synchronization of internal and external data using MS SSMS. 🔹 ETL & Process Digitization – Data transformation using MS SSIS. 🔹 Data Warehouse & Cubes – Data Mart modeling with MS SSAS & MS SSMS. 🔹 Reporting & Visualization – Automated insights with MS SSRS & Power BI. 🔹 Documentation – Ensuring scalability and maintainability. 🔹 SCRUM methodology, applied using Redmine. 🚀 Phase Two – Predictive Analytics & Knowledge Engineering: A Knowledge Engineering System was developed using RapidMiner, creating a decision tree model with 90% accuracy in predicting students unlikely to enroll. Attribute derivation was key, as initial data lacked predictive power. Key engineered features included: 🔹 Distance between the student's home and institution. 🔹 Number of peers from the same high school placed in the program. 🔹 Placement ranking (option in which they were admitted). 🔹 Program popularity in that academic year. This system enabled targeted student engagement, boosting conversions and reducing vacancies. For greater accuracy, separate models for each placement phase used historical data to refine predictions.
💡𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: I was invited to be a teacher for a high school program and a coordinator for a professional internship in a work environment. 📝 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: The internship focused on computer systems programming, with trainees divided into two teams: 🔹1. Backend & Database: API development, data persistence, and quality management. 🔹2. Web Scraping & Data Extraction: Using scripting, object-oriented programming, DOM manipulation, and JSON. 🔹 This structure created a full-stack project covering front-end, back-end, and database development. 🔹 To ensure all trainees worked on every aspect of the project, teams were rotated. 🔹 Biweekly meetings were held to discuss integration challenges and encourage collaboration. 🛠 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: To help trainees focus on programming concepts, I developed a web scraping tool with a four-panel interface displaying: 🔹1. Web Page Being Scraped – A live preview of the target website from which data was being extracted, allowing trainees to see real-time changes. 🔹2. Website Code – The HTML and JavaScript structure of the web page, helping trainees understand how elements were organized and where to extract data from. 🔹3. Coding Implementation Area – A built-in code editor where trainees could write and modify their scraping scripts, using programming languages such as Python or JavaScript. 🔹4. Terminal for Output Results – A console displaying the extracted data in real-time, allowing trainees to verify, debug, and refine their scraping logic efficiently. Additionally, I created an API and a database to store scraped data. By applying the acquired knowledge, the trainees successfully extracted over 100,000 leads. Finally, I prepared evaluation reports and conducted an oral defense to assess each trainee's performance.
📝 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Co-founded a startup with two schoolmates, developing MAPO.PT, an agricultural trading platform initially designed as a C2C & Digital Marketing solution. The platform was accessed from over 100 countries and received three awards for Entrepreneurship and Technological Innovation. Strategic partnerships were established with associations, cooperatives, Portuguese communities abroad, and PALOP countries. Over time, the platform evolved from C2C to B2C, expanding its business model. 🛠 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 🔹 Full-stack development from scratch: HTML, CSS, JavaScript (jQuery, Node.js, Angular), PHP (Laravel), MySQL, Redis, MongoDB. 🔹 Version control & repositories: Git, Bitbucket. 🔹 Agile SCRUM methodology with project management using Trello. 💡𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: 🔹 Requirement Analysis – Conducted market studies with farmers to define business needs. 🔹 Software Development – Designed and implemented the platform architecture, frontend, backend and database. 🔹 Business & Technical Presentations – Represented MAPO.PT at entrepreneurship competitions, agricultural expos, and technology events. 📈 Extended Success & Business Expansion: The success of MAPO.PT led to business expansion, attracting outsourcing contracts for custom software development, including: 🔹 Car stock management systems for dealerships. 🔹 Agricultural production management software. 🔹 E-commerce platforms. 🔹 Centralized information systems for assembly lines (tablet-integrated). 🔹 Web solutions