About
I'm a results-driven data professional with expertise in building enterprise-scale data infrastructure and analytics solutions. Currently serving as a Business Intelligence and Strategy Analyst at Houston Dynamo FC, I specialize in designing ETL pipelines, data warehousing, and creating automated workflows that drive revenue growth and operational efficiency.
With experience processing 450M+ records across cloud platforms like AWS and Databricks, I've successfully integrated diverse data sources to support critical business functions—from ticket sales and fan engagement analytics to revenue optimization strategies that have contributed $675K in new business. My technical toolkit spans SQL, Python, PowerBI, Tableau, and Salesforce, enabling me to bridge the gap between raw data and strategic decision-making.
I got into this industry due to my love of sports analytics, where I am able to combine my love for data with my enthusiasm for all sports. Through personal projects on my GitHub—including NBA player development prediction models and NFL team performance analysis—I've been building expertise in sports-specific data science applications. My ultimate goal is to break into the sports analytics field, contributing to front office decisions, player evaluation, and strategic team building through advanced data insights.
Skills
- SQL & Databases: Agilitek, Databricks, AWS, Snowflake
- Data Viz: Tableau, Power BI, Jupyter, Excel
- Programming: SQL, Python, R, Batch, Git
- Automation: Zapier, Power Automate, ChatGPT API
- CRM & Marketing: Salesforce, SFMC, Conversica AI
- Survey & Analysis: Qualtrics, SurveyMonkey, Intellistack
Experience
Lead ETL processes, Salesforce CRM, and BI strategy. Transform raw data, build dashboards, and optimize ticketing, marketing, and fan engagement workflows.
Managed ETL processes, built dashboards, analyzed surveys, and integrated legacy data systems to improve operational insights across digital and sales teams.
Work Projects
View All ProjectsPersonal Projects
Advanced sports analytics portfolio demonstrating end-to-end data pipelines, machine learning, and interactive dashboard development across NBA and NFL datasets.
NFL Data Engineering Pipeline
Complete end-to-end data engineering project featuring Docker containerization, PostgreSQL database, ETL pipeline automation and interactive Streamlit dashboard. Demonstrates production-ready data architecture with real NFL team data extraction, transformation, and visualization.
Tech Stack: Docker, PostgreSQL, Python, Streamlit, FastAPI, ETL Automation, Data Visualization
NBA Player Development Analytics
Machine learning pipeline for tracking player improvement and identifying breakout candidates. Features multi-season development trends, breakout probability modeling, and interactive dashboards for front office decision-making.
Tech Stack: NBA API, Python, Predictive Modeling, SQLite, Plotly Dash
NBA Team Chemistry & Lineup Analytics
Comprehensive team performance analysis focusing on lineup efficiency and player combinations. Built custom chemistry scoring algorithms and multi-dimensional performance mapping for coaching staff optimization.
Tech Stack: NBA API, Advanced SQL, Team Performance Modeling, Interactive Visualization
NBA Shot Selection & Efficiency Analytics
Spatial shooting analysis with interactive shot charts and zone efficiency breakdowns. Developed shot selection quality scoring and heat map visualizations for player development insights.
Tech Stack: NBA API, Spatial Analysis, Heat Maps, Shot Chart Visualization, Custom Metrics
NFL Team Performance Analytics
Advanced NFL analytics pipeline analyzing team efficiency through Expected Points Added (EPA) and success rate metrics. Built coaching-focused dashboards for strategic decision-making and performance evaluation.
Tech Stack: NFL Data API, Python, EPA Analysis, Success Rate Modeling, Coaching Dashboards
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