Eron Oliveira

My journey into data began with a passion for solving business problems through data-driven insights. While working in various companies and professional projects, I’ve also developed personal projects outside of my day-to-day work, focusing on data science, analytics, and engineering using public datasets.
Here, you’ll find a collection of these personal projects that highlight my skills and the tools I use to tackle real-world challenges. If you’re looking for someone to bring data-driven solutions to your team or project, feel free to reach out via the links below."

About me

My name is Eron Oliveira

Data Engineer and BI Developer committed to empowering organizations through data-driven strategies.
With a strong foundation in dimensional modeling, cloud solutions, and business intelligence, I design and deliver scalable data solutions that drive informed decision-making.
My expertise spans Power BI, Azure technologies, and modern data architectures, with a focus on creating impactful dashboards and semantic models.
I believe in the power of collaboration, agility, and continuous learning to deliver value and foster innovation in every project I undertake. I have a solid ability to extract insights from data and communicate them effectively to stakeholders.
I'm always looking for new opportunities to use my skills and expertise to make a positive impact.

Skills

Work Experience

Opulent Cloud | Data Engineer (05/2024 – Present)

At Opulent Cloud, I develop high-performance dimensional models and ETL pipelines using Microsoft Fabric, Tabular Editor, and Python. I implement CI/CD processes through Azure DevOps to streamline data operations and ensure automation. Additionally, I manage cloud costs through FinOps practices, optimizing Azure resources, and create executive dashboards in Power BI to support strategic decision-making.

Natixis in Portugal | Senior Reporting Officer (02/2023 – 05/2024)

During my time at Natixis, I focused on building complex data models and automating ETL processes using Power BI and Power Query (M Language). I was responsible for producing regulatory and governance reports for both internal and external stakeholders. I also led reporting projects within the Conduct and Controls domains while mentoring junior team members to ensure quality and efficiency in our deliverables.

Findmore Consulting | Data Analyst (03/2022 – 02/2023)

At Findmore Consulting, I designed and implemented efficient ETL processes using SQL, SSIS, and Power BI Service. I created dynamic dashboards and KPIs using DAX and M Language, which provided valuable insights for clients. My work also involved enhancing user experience in data visualization through tools such as Power BI, SSRS, and Figma.

Brazilian Federal Court (TRF4) | Data Analyst (04/2020 – 03/2022)

While working for the Brazilian Federal Court, I analyzed institutional data using MicroStrategy to improve operational processes. I redesigned dashboards to track over 30 KPIs, which contributed to a 5% reduction in operational costs (~€500,000). Additionally, I used data storytelling to effectively communicate insights, promoting a data-driven approach within the organization.

Data Engineering Projects

New York City payroll project

The City of New York aims to build a Data Analytics platform on Azure Synapse Analytics to achieve two key objectives:

• Analyze the allocation of the City's financial resources, focusing on overtime expenses.
• Provide public access to data on salary and overtime pay for municipal employees.

The primary goal is to create dynamic, automated, and monitored data pipelines for efficient operations. The project team includes the city’s quality assurance experts, who will test and enhance the data quality.
The source data, stored in Azure Data Lake, will be processed in the NYC data warehouse on Azure Synapse Analytics. The datasets include CSV files containing employee master data and monthly payroll information from various City agencies.

Main tools used:

• Azure Data Lake Gen2
• Azure SQL DB;
• Azure Data Factory;
• Azure Synapse Analytics;

• + Info

Pricing Strategy for E-commerce

I developed a Python-based web scraping tool to gather information from key competitors of an e-commerce company aiming to enter the U.S. apparel market. The code performs ETL (Extract, Transform, Load) operations, extracting and transforming the data into a database, then calculates the pricing results based on medians. The final results are delivered through a Streamlit application.

Main tools used:

• Python;
• Git and Github;
• JupyterLab;
• SQLite;
• Webscrapping libraries (Beautiful Soup);
• Cron job;
• Streamlit.

• + Info

Business Intelligence Projects

Recency, Frequency, and Money (RFM) Analysis

Sales B2C Dashboard

Corporate Sales Dashboard

Pricing Strategy for E-commerce

I developed a Python-based web scraping tool to gather information from key competitors of an e-commerce company aiming to enter the U.S. apparel market. The code performs ETL (Extract, Transform, Load) operations, extracting and transforming the data into a database, then calculates the pricing results based on medians. The final results are delivered through a Streamlit application.

Main tools used:

• Python;
• Git and Github;
• JupyterLab;
• SQLite;
• Webscrapping libraries (Beautiful Soup);
• Cron job;
• Streamlit.

• + Info

Data Science Projects

Cross-Selling of Insurance Products

I developed a solution to identify whether an insurance company's clients were interested in purchasing a second product, in this case, car insurance. Based on this solution, the sales team could prioritize the most interested customers, optimizing the sales campaign and increasing efficiency.

Main tools used:

• Python, Pandas, Matplotlib, and Seaborn;
• Git and Github;
• JupyterLab;
• Machine Learning Models (KNN Classifier, Logistic Regression, Random Forest Classifier, Naive Bayes Classifier, XGBoost Classifier);
• Heroku;
• Google Sheets.

• + Info

Sales Volume Forecasting

I developed an intelligent model that uses revenue and key characteristics of a retail chain to forecast sales performance over the next six weeks. The results are delivered through a Telegram Bot, streamlining and simplifying access to the information.

Main tools used:

• Python, Pandas, Matplotlib e Seaborn;
• Git e Github;
• JupyterLab;
• Machine Learning Models (Linear Regression, Regularized Linear Regression Model – LASSO, Random Forest Regressor, XGBoost Regressor);
• Flask;
• Heroku;
• Telegram.

• + Info

Contacts

Feel free to reach out with any questions or suggestions. I'm happy to assist!

• eron.oliveira@gmail.com
• LinkedIn
• Github