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Simply lovely
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Simply lovely
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RafaelParraCoelho/README.md

Hello there 👋

I'm Rafael Parra Coelho

🎓 Aspiring Data Engineer | 🐍 Python & SQL | ☁️ Cloud Enthusiast

🚀 About Me

  • 🎓 Currently pursuing a Bachelor's degree in Information Systems
  • 📊 Focused on data analysis and data engineering fundamentals, with hands-on projects
  • 🔧 Experience building simple data pipelines and ETL workflows using Python and Airflow
  • 💾 Solid foundation in SQL, relational databases, and data organization concepts
  • 🐍 Proficient in Python for data analysis, automation, data transformation, and API consumption
  • 🌐 Knowledge of web technologies to support data-driven applications and integrations
  • 📈 Interested in turning raw data into insights through well-structured data processes

My goal is to grow as a Data Engineering professional, contributing to reliable data solutions while continuously learning and improving my technical skills.

🔧 Tech Stack

Core Data Engineering

  • Languages: Python (data analysis and automation), SQL (queries and joins)
  • Orchestration: Apache Airflow (simple pipelines)
  • Processing: Apache Spark (concepts and basic usage)
  • Databases: PostgreSQL, MongoDB (basic), SQLite
  • Cloud Platforms: Google Cloud Platform (BigQuery, Cloud Storage)
  • Data Tools: Pandas, NumPy, Psycopg2

Development & Integration

  • APIs: REST API development and integration (Requests, Flask/FastAPI)
  • Web Scraping: BeautifulSoup, Selenium
  • Visualization: Matplotlib, Seaborn
  • Version Control: Git, GitHub
  • Additional: JavaScript, HTML, CSS, C, C++, Linux

🧠 Data Engineering Skills

  • 🔄 ETL/ELT Pipeline Development: Development of simple and automated data workflows using Python
  • 📥 Data Ingestion: Web scraping, API integration, and batch data collection
  • 🧹 Data Transformation: Data cleaning, normalization, aggregation, and enrichment with Pandas
  • 🗄️ Database Management: Working with relational databases, schema organization, and SQL queries
  • ☁️ Cloud Data Solutions: Using BigQuery for analytical queries and data exploration
  • 🔍 Data Quality: Basic validation, error handling, and data consistency checks
  • 📊 Analytics Support: Preparing datasets to support analysis, dashboards, and reporting

💡 "From failure to success, from success to the next attempt"

Pinned Loading

  1. rafaelparra.github.io rafaelparra.github.io Public

    I am a Bachelor’s student in Information Systems with hands-on experience in data, automation, and web development. My background is strongly focused on Python and SQL, working with data analysis, …

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    HR Turnover Analysis using Python, SQL and Power BI to identify risk factors and reduce attrition by 30% ```

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  3. f1_2025_analysis f1_2025_analysis Public

    This project collects data from the 2025 Formula 1 season using Wikipedia, saves standings and race calendar into CSV files, and generates visualizations for drivers' and teams' scores

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  4. File-Miau-Bistro File-Miau-Bistro Public

    O Filé Miau Bistrô é um projeto web focado em treinar habilidades de front-end moderno com HTML, CSS e JavaScript.

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  5. Currency_Converter_API Currency_Converter_API Public

    This project is a simple currency converter that uses a public API to fetch real-time exchange rates. It allows the user to convert values between different currencies, store a conversion history, …

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  6. Data_Quality_Monitor Data_Quality_Monitor Public

    A Python script for quick **data quality analysis** on CSV files. It checks for null values, duplicates, and data types — helping identify common issues in datasets easily.

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