Leading enterprise data initiatives with 6 years of experience delivering AI/ML solutions and managing cross-functional teams of 30+ professionals.
Specializing in modern data stack implementations with Python, SQL, dbt, Airflow, Snowflake and cloud platforms.
A highlight of my recent technical work and architectural decisions.
This project implements a data pipeline for the Adventure Works dataset using dbt Core and BigQuery. It includes data ingestion, transformation, and modeling following best practices in data engineering. The project features a comprehensive data model with staging, intermediate, and mart layers, along with automated testing and documentation.
A comprehensive overview of my technical skills and technology stack.
A collection of data engineering and data science projects showcasing modern data stack implementations.
This project implements a data pipeline that extracts data from BigQuery, validates it using Pydantic models, and loads it into Streamlit.
This project follows the "poor man's data lake" concept, leveraging open source tools like DuckDB, dbt and Airflow to process data from the Spotify API.
This project focus on processing a billion rows with different python libraries like pandas, polars and duckdb presented in a simple dashboards with streamlit.
This project showcases skills in managing databases using PostgreSQL within a Docker environment and in performing data transformation using dbt.
This project leverages dbt (data build tool) and DuckDB to analyze air quality data sourced from the World Health Organization (WHO).
This project uses OLTP and OLAP databases with MySQL and PostgreSQL, ETL pipelines with Python, orchestration with Airflow and predictive modeling using Apache Spark.
In this final capstone project, I followed the CRISP-DM process model, including data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation and results reporting.
As a data engineer working for an international financial analysis company, my job in this project was to collect financial data from various sources such as websites, APIs, and files provided by financial analysis firms.
This project contains an exploratory data analysis of crime, socio-economic and school performance for the city of Chicago using a public dataset.
This project demonstrates how to utilize Apache Spark to convert Parquet file data into a CSV format and subsequently train a Random Forest model.
This project is focused on extracting financial data of popular stocks such as Tesla, Amazon, AMD, and GameStop using Python libraries and web scraping.
In this project, I determined the market price of a house given a set of features, using attributes or features such as square footage and number of bedrooms.
In this project, I used the weatherAUS.csv dataset which includes weather observations from 2008 to 2017. The implemented algorithms were Linear Regression, Decision Tree, Logistic Regression, and SVM. I evaluated each model using performance metrics such as Accuracy Score, Mean Squared Error and R2-Score.