Lucas Rabay Butcher is a Data Engineer Intern at V360, currently located in João Pessoa, Paraíba, Brazil, and is a student at Universidade Federal da Paraíba studying Data Science and Business. With two years of experience, Lucas is employed, open to work, and seeking full-time roles, particularly interested in technical architecture. Their projects include building scalable Python and Apache Airflow ETL pipelines at V360, managing data ingestion into AWS S3, and developing tools like pt2bash for natural language to Bash translation. Lucas is also looking for co-founders (technical or business) and founding engineer opportunities.

Technical architecture, data engineering, scalable ETL pipelines, Apache Airflow, AWS S3, AWS QuickSight, machine learning engineering, natural language processing, T5 fine-tuning, computer vision, YOLOv8, full-stack development, Django Ninja, TypeScript, Expo, founding engineer opportunities, co-founder collaboration.

Building scalable Python and Apache Airflow ETL pipelines at V360, managing data ingestion into AWS S3 and visualization via QuickSight. Developed pt2bash, a T5 seq2seq model fine-tuned for natural language to Bash translation. Built a vehicle and signal detection pipeline using YOLOv8. Developed Warao, a language-learning app featuring a Django Ninja backend and TypeScript/Expo frontend. Previously implemented ML models and data workflows at Grupo Cajueiro and PBGÁS.

I am currently a Data Science Intern at Sicredi Evolução, one of Brazil’s largest credit unions, where I contribute to projects and research that support business decision-making. My responsibilities include building data pipelines, developing machine learning models and dashboards, designing and querying databases, and extracting valuable insights from data. I also work closely with business areas on initiatives such as Open Finance and personalized offers. In parallel, I am pursuing a Bachelor’s degree in Data Science for Business at the Federal University of Paraíba (UFPB), where I contribute in some extension and research projects!

My areas of interest are mainly Data Science, Artificial intelligence, Financial Markets and Software Engineering.

I have a strong interest in capital markets and am a member of the Financial Market League at UFPB. I also contribute to an extension project at UFPB, called Bridge Sci-Hub, collaborating with leading professors in Data Science and Finance to develop quantitative models, data-driven dashboards and public reports to all society. Furthermore, I contribute to LEMA, the Applied Modelling Studies Lab, at UFPB, developing Data Pipelines, Machine Learning Models and Data Engineering.

I'm a Software Engineer Intern at Dhauz, currently working on the development of Tracelab, a startup focused on personal data security. Also working as a researcher at Dharma-AI, transcribing plenary sessions of the Brazilian Supreme Court (STF) and classifying the speaking minister. I'm in my 2nd year studying Ciência da Computação at Universidade Federal da Paraíba.

Software/Data Engineering, Machine Learning and AI integrations with systems.

In addition to the projects I'm currently working on, I worked at Estudo Play, developing a highly accurate automated ENEM essay corrector for public school students. Last year, Estudo Play corrected over 4 million essays using AI, achieving 224k simultaneous corrections in a single day, serving over 1 million users.

I’m a Data Science student at UFPB and a member of TAIL (Technology and Artificial Intelligence League), where I actively develop projects in data and AI. I have experience in data analysis and machine learning, working with real datasets to perform data cleaning, exploratory analysis, and build models that generate useful insights. I’ve also explored NLP projects, applying techniques for text processing and classification. Alongside that, I’m developing skills in tools like PostgreSQL, PyTorch, and YOLO. Recently, I’ve been growing my interest in computer vision and data engineering, focusing on building more complete, end-to-end solutions.

I’m increasingly interested in machine learning, especially in building models that solve real-world problems. I’m also developing a strong interest in computer vision, exploring object detection and deep learning approaches, and looking to deepen my understanding through practical projects and collaborations.

A data analysis project in an educational context, where I explore datasets, handle missing data, and generate insights to support decision-making. Machine learning experiments using scikit-learn and PyTorch, focusing on how preprocessing choices impact model performance and potential bias. NLP projects involving text classification and fake news detection, leveraging libraries such as NLTK and spaCy. Computer vision projects, including gesture detection using YOLO and experiments with models fr