Data engineering and analytics projects focused on SQL, APIs, validation, and reproducible analysis workflows.
SQL and Data Engineering Foundations
This section highlights projects that connect database thinking with practical analytics: requesting data, validating loads, preparing analysis-ready tables, writing SQL queries, and communicating results through clear visual summaries.
My focus is on building reproducible workflows that can be reviewed, rerun, and extended: from raw data access to SQLite storage, SQL-based metrics, exported outputs, and documented findings.
Restaurant Inspection Risk & Compliance Analytics
Built an API-to-SQLite analysis pipeline for 311k+ Chicago food inspection records, using SQL and Python visualizations to identify inspection failure patterns by outcome, facility type, time, and ZIP code.
Tools: Python, pandas, SQLite, SQL aggregation, Socrata API, data validation, CSV exports, and README-ready visual communication.
Project Signals
- Public API access and pagination planning
- SQLite table creation and CSV-to-database loading
- SQL aggregation, conditional counts, percentages, and time-based summaries
- Data validation checks and reproducible analysis exports
- Visual summaries and documented interpretation limits