On this page you will learn a little about my journey and my skills in Data Science, Analysis and Engineering, Artificial Intelligence and Cloud Infrastructure, through projects with public data.
I have a degree in Data Science from Uninter and work as a Data Engineer II at Sapiensia Tecnologia. Below you will learn about the tools and skills I possess to solve problems through Data Science, Data Analysis, Data Engineering, Artificial Intelligence and Infrastructure, as well as some professional experiences. Feel free to contact me through the links at the end of the page.
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AZ-900
DP-900
Astronomer
Implementation of data pipelines, dashboards, specialized LLMs, and automations with Python in Serverless architecture. Experience in Azure, including disaster recovery plans and certifications.
Development of data processing software, churn prediction models, and data ingestion pipelines using Python and Google Cloud.
Complete data pipeline focused on data engineering, with automated ingestion of public information from all federal deputies. The data includes biography, mandates, expenses and parliamentary activity, extracted via official API. The architecture implements a modern ELT approach with Snowflake and dbt . Daily incremental ingestion is orchestrated with Airflow and stored in S3 in Parquet format. Transformations follow robust dimensional modeling patterns (SCD Type 2). The project ensures end-to-end scalability, automation and data quality.
This project used the Databricks Data Lakehouse architecture to manage data in layers (Raw, Bronze, Silver, and Gold) and simulate ingestion scenarios with CDC (Change Data Capture). The data, from a Kaggle dataset, was enriched to create a complete pipeline, from ingestion to business analysis. I implemented data governance with Unity Catalog, orchestration with Databricks Workflows, and continuous integration via GitHub Actions. The project consolidated skills in data pipelines, automation, and analysis with the Medallion architecture, optimizing the use of data for insights and analytical applications.
In this project, I explored Azure Artificial Intelligence tools to build a chatbot specialized in Azure using GPT-4. I copied the data from the Azure documentation on GitHub to the Storage Account, used Azure AI Search to perform embedding and indexing of the content, and Azure OpenAI to build the chatbot in an App on Azure. The goal is to provide accurate and contextualized answers about Azure services and functionalities.
In this project, I explored Azure Artificial Intelligence tools for optical character recognition (OCR), such as Azure Computer Vision and Azure AI Document Intelligence. I used Python to develop a Telegram bot that processes images sent by the user, returning the extracted text and the confidence interval for each recognized word. I implemented dynamic settings in the bot, allowing adjustment of parameters such as the minimum confidence level to accept words and the application of pre-processing. This project demonstrates skills in API integration, image processing and the creation of interactive interfaces with bots.
I was a semifinalist in the Stone Data Challenge 2022. In this Stone challenge, my task was to use historical data from a loan program from 2019 to April 2022 from 14,700 clients. The business problem was related to contacting clients who were behind on payments. The question to be answered was: What is the ideal curve of times we should contact a client? To answer it, I used Python and Power BI to answer the question with data analysis.
I used Python to create a Machine Learning model to predict the sales of each of the 3,000 registered stores in the next 6 weeks. The model was put into production and can be requested via API by Telegram, just needing internet access to use it. The model had a 90% prediction of the real value, allowing the CFO to make decisions based on the future revenue of each store unit and thus be able to make investments without losses.
I used Python to create a Machine Learning model to rank the customers most likely to purchase a new product (cross-sell strategy). With an accuracy of 33.5% for the top 20,000 customers in the database, the sales team is able to reach interested parties with much less cost.
I used Python to create a Machine Learning model to find the "Insiders," the best customers of the company. The objective of this project was to group customers with similar behaviors so that the business team can build personalized actions, based on the characteristics of each cluster.
I used Python and Power BI to perform exploratory data analysis and thus confirmed or not some hypotheses about the business, resulting in insights for better business performance. The analysis aimed to increase the revenue of the fictitious company, House Rocket, which works with the buying and selling of real estate, finding the best times to buy or sell the property.