Analytics Engineer — dbt • BigQuery • SQL • Python
I build data infrastructure that turns raw, messy data into clean, trusted models that teams can actually use. My focus is analytics engineering — designing dbt transformation pipelines, modeling staging and mart layers in BigQuery, and bridging the gap between data engineering and business intelligence.
Currently completing my M.S. in Business Analytics at Grand Canyon University (4.0 GPA) while building portfolio projects on a modern cloud stack. I hold the Microsoft Azure DP-900 certification and am actively targeting Analytics Engineer roles.
End-to-end analytics engineering project simulating a fintech app's data infrastructure.
Python-generated datasets are loaded via dbt seed into BigQuery, then transformed through
a three-layer pipeline — raw → staging → mart. Mart models include
dim_users, fct_transactions, and fct_monthly_spending,
with a schema.yml defining tests and column-level documentation.
End-to-end analytics engineering project transforming raw user, event, and subscription data into analytics-ready models. Built with Python and SQLite across a structured pipeline — raw ingestion through SQL modeling — to support tracking of user activity, revenue, and retention metrics.
View on GitHub →Analytics pipeline built with Python, SQLite, and SQL to analyze transaction data and identify potential fraud patterns. Implements a multi-layer data modeling approach with transformation workflows designed to support analytical queries and a four-page Power BI dashboard.
View on GitHub →Analyzed hotel booking data to identify cancellation trends and key drivers. Created visualizations and insights in Tableau to support predictive modeling and business decisions.
View on GitHub →Explored social media engagement data using Python, pandas, and SQL to uncover trends in performance. Built visualizations to highlight key metrics and support data-driven marketing insights.
View on GitHub →