Senior Backend Engineer (Go · Python) | Data Engineer | GIS & Geospatial Systems | AWS · Spark · GeoPandas | Remote

Hernán Galileo Cabrera Garibaldi  • Peerlist

REST API built with Python, GeoPandas, PostGIS and GeoJSON that ingests, models and serves structured geospatial data for Mexico City's public transit network. Developed as part of Urban Planning research at UNAM.

CDMX Public Transit REST API

Academic research combining computational graph theory with urban planning to evaluate the impact of a circular (ring-shaped) public transit corridor on Mexico City's Metropolitan Area network. The study models the full ZMVM multimodal transit network as a directed weighted graph G=(V,E) using GTFS open data, and applies graph algorithms to calculate 8 efficiency and vulnerability indicators — including Average Travel Time (T), Detour Index (DI), Betweenness Centrality B(v), and Geometric Robustness (ΔE). Simulation results show the proposed ring corridor would reduce average travel time by ~24% and cut hub vulnerability by ~47% at critical transfer nodes. Tech stack: Python · Go (Golang) · GeoPandas · PostGIS · NetworkX · GTFS feeds · LaTeX · GitHub Actions CI/CD Developed as part of the Master's in Urban Planning at UNAM (2025–2027), supervised by Dr. David López Flores. Powered by two original open-source engines: Apimetro (geospatial transit data API) and VFTModel (graph analytics engine).