Wildomar, California, United States
I’m a Computer Science graduate with hands-on experience using SQL, Python, Excel, and Power BI to clean, analyze, validate, and visualize data. My work combines data analytics with strong data engineering fundamentals, allowing me to work across the full data lifecycle—from raw data extraction and transformation to analysis, visualization, and communicating insights. Through my projects, I’ve worked with large structured and unstructured datasets, including 500K+ retail transactions, 408K+ municipal building permits, planning documents, REST APIs, and NFL play-by-play data. Some of my recent work includes: • Analyzed 500K+ retail transactions and $8.74M in revenue, identifying $1.02M associated with customers inactive for more than 90 days and 192 high-value customers classified as at risk. • Used SQL and Python to analyze customer retention, RFM segments, product cancellations, revenue trends, geographic performance, and product affinity. • Analyzed 408K+ municipal building permits and 8,003 planning records across all 15 Los Angeles council districts, identifying differences in permit processing times, development activity, and unresolved permit aging. • Built Power BI dashboards to communicate KPIs, trends, customer behavior, operational performance, and statistical findings through interactive visualizations. • Applied statistical analysis to 107 NFL fourth-down decisions using EPA, WPA, and hypothesis testing to evaluate differences between aggressive and conservative decisions. • Built supporting ETL pipelines and dimensional data models using PostgreSQL, Python, Databricks, PySpark, and Delta Lake, with data-quality validation and root-cause analysis throughout the workflow. I enjoy turning raw data into answers to meaningful questions—whether that means identifying a customer retention problem, investigating operational performance, validating the reliability of a dataset, or building a dashboard that makes complex information easier to understand. I’m currently seeking entry-level opportunities in Data Analytics, Data Analyst, BI Analyst, or Reporting Analyst roles, while also remaining open to Analytics Engineering and data-focused engineering opportunities. Core skills: SQL • Python • pandas • Power BI • DAX • Excel • Power Query • PostgreSQL • Data Visualization • Statistical Analysis • Data Cleaning • Data Quality • ETL/ELT • Databricks • PySpark