Four-month internship focused on identifying and analyzing locations eligible for BEAD (Broadband Equity, Access, and Deployment) grant funding across the United States. The work combined QGIS and SQL to evaluate household density, business presence, and cost optimization criteria, supporting decisions on where fiber infrastructure investments could be most impactful.

Tasks

• Identified and analyzed BEAD-eligible locations using QGIS and SQL, weighting household density, business presence, and cost factors.
• Collaborated with cross-functional teams so that the GIS outputs fed directly into broader business development strategies.
• Automated repetitive GIS workflows through SQL and QGIS to improve efficiency, reproducibility, and accuracy.
• Optimized the analysis of spatial layers to accelerate decision-making.

Learning Objectives

• Practical integration of GIS with PostgreSQL/PostGIS for advanced spatial data management and analysis.
• Designing automated workflows in QGIS and SQL that reduce manual effort and human error.
• Translating technical geospatial outputs into actionable business insights for non-technical stakeholders.
• Understanding how geospatial evidence shapes investment strategies, particularly for broadband grant funding.

Lessons Learned

• Automation pays off — investing time in reproducible SQL and QGIS pipelines compounds across every later analysis.
• Communication matters — adapting technical language to different audiences is as important as the analysis itself.
• Adaptability — datasets vary widely in format and quality, and flexibility is required to deliver consistent results.

Final Reflection

This internship bridged academic knowledge with professional practice in GIS and data sciences. Beyond technical growth, it strengthened teamwork, communication, and problem-solving skills — all directly relevant to a career in digital earth applications and geospatial analysis.

Tech Stack

QGIS, SQL, PostgreSQL / PostGIS, Python, Geopandas