This repository is a portfolio of data engineering projects I have completed. It demonstrates my skills in building, managing, and optimizing data pipelines. The projects cover end-to-end data workflows, including data ingestion (ETL/ELT), data warehousing, and the design of scalable data architectures

In this group project simulating a real-world setting, we built a scalable ETL pipeline to process daily CSV transactions into a centralized PostgreSQL database. We used Docker, Grafana for visualization, and later implemented AWS cloud services to deploy a scalable, cloud-based ETL system.

Data automation involves automating the extraction, transformation, and loading (ETL) processes to streamline data workflows. GitHub Actions enables automated execution of tasks, such as building, testing, and deploying code, in response to events. This integration simplifies continuous deployment and ensures repeatable data pipeline operations

Building a model data warehouse with SQL Server, Including ETL Processes, data modeling and analytics

End-to-end data pipeline for hospital readmission analytics using Snowflake, dbt, Airflow, and Power BI.

FleetFluid is a Python library that simplifies data transformation by letting you use AI-powered functions without writing (and hosting) them from scratch.

🏥 Analyze hospital readmissions with a data pipeline for insights on risk factors, improving patient outcomes using modern tools and predictive models.