Submission of Candidate Application for the role of Data Engineer at The Eviction Lab of Princeton University
In order to apply to the Data Engineer role at the Eviction Lab, I am spinning up a concise data pipeline to meet the fourth criteria laid out in the listing. Please review the google collab script.
This project demonstrates a beginner's level of proficiency with a handful of the tools mentioned in the JD. I use those tools to build a civic data pipeline of sorts, combining:
- Legal Services Corporation eviction filings data (weekly county-level)
- U.S. Census Bureau ACS 5-Year data (multi-year, multi-variable)
- Optional integration with Zillow housing data
Using Python, the Census API and hard copy of the Legal Services Corp, and DuckDB SQL in Google Colab, the project:
- Ingests, normalizes, and joins multiple sources of data
- Aggregates eviction filings to annual levels
- Enriches data with median income, poverty rate, and population
- Outputs a clean dataset for analysis and visualization
The final product enables county-level comparison of eviction trends and economic hardship across all 50 U.S. states and D.C.