I am a data professional focused on building efficient data pipelines, orchestrating workflows, and enabling data-driven decisions.   My career goal is to consolidate myself as an Analytics Engineer or Data Engineer, applying best practices in data modeling, transformation, and automation.  

I am currently deepening my skills in both data engineering and backend development, actively seeking new challenges and positions as a Data Engineer or Backend Developer.

I enjoy solving real problems with clean code, reproducible pipelines, and data solutions that bring value to business.  

• SQL for modeling, querying, and performance optimization  
• Python with Pandas, NumPy, PySpark, and automation scripts  
• Apache Airflow for workflow orchestration  
• ETL/ELT with Meltano, Embulk, and dbt  
• Containerization with Docker and version control with Git  
• Databricks Delta Lake for analytical workflows  
• Cloud: AWS (S3, Glue, Lambda) and GCP (BigQuery, Cloud Functions)  

• Dashboards with Power BI, Metabase, and Looker Studio  
• Advanced Excel for reporting and analysis  
• Clear and concise data storytelling for stakeholders  

Modular pipeline with multiple sources (PostgreSQL & CSV), fully containerized:

• Data extraction with Embulk (13 tables)  
• Custom Meltano tap for CSV ingestion  
• Load into PostgreSQL using JSONL and CSV  
• Orchestration with Airflow DAGs  
• Automation with Shell Scripts and Makefile  

RESTful API in Flask with SQLite and Docker. Includes CRUD operations, Postman tests, and modular route separation.  

End-to-end pipeline for HR data: collection with Python, loading into BigQuery, and visualization with Power BI.  

Machine Learning project using XGBoost, SMOTE balancing, and model explainability. Covers EDA, training, evaluation, and delivery.  

• Analytics & Data Engineering Track — Practical projects in ingestion, transformation, and orchestration  
• DataCamp Portfolio: datacamp.com/portfolio/matheusvazdata  

• LinkedIn: linkedin.com/in/matheusvazdata  
• GitHub: github.com/matheusvazdata  
• E-mail: [email protected]  

This space showcases part of my journey in data engineering and analytics. Always open to collaborations, learning, and new challenges.