Luiz Jesus

End-to-end Modern Data Platform built to transform e-commerce data into analytics-ready datasets using a scalable cloud architecture.

The solution covers the complete data lifecycle, from ingestion and cloud storage to transformation, data quality, governance, dimensional modeling, and BI consumption.

Key components:
• Azure Data Factory (ADF)
• Azure Data Lake Storage Gen2 (ADLS)
• Snowflake
• dbt Core
• SQL
• Power BI
• Medallion Architecture
• ETL/ELT pipelines
• Dimensional modeling
• Data Quality tests
• Data Lineage
• RBAC and data governance

Azure Data Factory handles ingestion and orchestration, ADLS Gen2 provides the landing layer, Snowflake serves as the analytical data platform, and dbt implements transformation and quality rules across Silver and Gold layers.

The Gold layer provides dimensional models and curated datasets ready for analytics and Power BI consumption.

Project developed as a practical end-to-end Data Engineering portfolio case.

End-to-end Data Engineering platform designed to process and analyze fraud detection data using a modern cloud architecture.

The solution was built on Google Cloud Platform and follows a Medallion Architecture approach, covering ingestion, transformation, data quality, orchestration, and analytics-ready datasets.

Key components:
• Google Cloud Platform (GCP)
• BigQuery
• Apache Airflow
• dbt Core
• Python
• SQL
• Medallion Architecture
• Incremental data pipelines
• Data Quality checks
• Data modeling and transformation

The project demonstrates the complete data lifecycle, from raw ingestion through validated and transformed datasets ready for analytics.

Project developed as a practical Data Engineering portfolio case.