I’m a hands-on analytics engineer and director building data products, models, and pipelines for the entertainment and music industry.
An ingredient recommendation system that brings together substitution knowledge, machine learning, and an interactive graph.
A demo of the Ingredient Recommender System, presented by me.
Watch on YouTube ↗Ingredient substitutes depend on context. The team built a prototype that lets users explore alternatives and adjust recommendations using cost and dietary filters.
I developed Python integration and graph-generation components, enriched recommendations with metadata, improved the interactive visualization, and resolved scoring and missing-data issues.
A connected workflow from source data to graph-ready JSON and a browsable interface. My work spans the data layer, presentation, debugging, and integration with teammates’ contributions.
Georgia Tech · CSE 6242, Data & Visual Analytics · Team project, 2025. Contributions described here are supported by the project’s Git history. The allergen filters are research-prototype functionality, not a validated safety assessment.
Original team poster. Open the image to inspect the full project.
Exploring sequel performance through franchise data, feature engineering, and comparative statistical modeling.
Can characteristics of a film and its franchise help explain whether a sequel earns more than the original—and the magnitude of that difference?
As the primary contributor to a five-person team project, I worked across data collection, preparation, feature engineering, modeling, and communication of the analysis.
Combining film and economic data, addressing missing financial records, and connecting model comparisons to business questions. This is an academic exploration, not a production forecasting system.
More than a decade in entertainment and music, with a career spanning licensing, analytics, and analytics engineering leadership.
Hands-on product building and individual-contributor engineering alongside leadership: developing data products, models, pipelines, and tools while shaping engineering standards, reviewing code, and mentoring colleagues.
dbt data models, Airflow pipelines, Looker business logic, and a Python / Streamlit tool for royalty reconciliation.
Process improvement, reporting, automation, team training, and licensing operations.
My career connects music and entertainment operations with hands-on analytics engineering. That background shapes how I approach a problem: understand the people and the business, then build the data products that help them move forward.
At BMG, I continue to do substantial individual-contributor work as a director. Alongside building products, I help teams strengthen their engineering practices and share what they learn.
Georgia Institute of Technology
Business Data Preparation and Visualization
Georgia Institute of Technology
SQL / Python / dbt / Airflow / BigQuery / LookML / Git / Tableau