Page 1 of 5

Junior Flores - Frontend Developer (GIS + ReactJS)

Compensation range discussed: $2,800 - $3,200 USD

Email: fmjg21@gmail.com | Phone: +51 983053500

Professional Profiles: LinkedIn | Portfolio | Github

Earliest date the candidate can join the company: 3 weeks after receiving an offer

Quick Recruiter Note: Based in Europe (studying English). Although confirming attendance at the 11 AM EST stand-up, has limited availability before that time.

Summary

Full Stack Developer with a Systems Engineering degree and over 5 years of experience building scalable, data-driven geospatial (GIS) applications. He has a strong background as a Geospatial Data Engineer at Development Seed, where he designed and deployed full-stack web applications. His expertise includes 5 years with React.js and extensive experience building interactive visualization tools using GIS libraries such as OpenLayers, Mapbox, and Leaflet. He developed a GIS Cadastral System for local governments using React and OpenLayers, and engineered a real-time vehicle-tracking platform using React and Mapbox. His backend proficiency in Python, Node.js, and PostgreSQL/PostGIS allows him to manage the entire data pipeline, from ETL processes to front-end rendering.

Skills

Meet Junior

This video from our screening interview provides a look into Junior's background and experience:

Candidate Snapshot

ReactJS Expertise

• Advanced (5+ years): Specializes in architecting scalable, data-driven geospatial applications. He has developed complex GIS interfaces from scratch, including a GIS Cadastral System for local governments built with and , and engineered a platform with React and Mapbox. His experience covers the full lifecycle, from processing raw geospatial data on the backend to rendering interactive visualizations on the frontend.

GIS Libraries Experience

• OpenLayers (Intermediate): Applied in a GIS Cadastral System for local governments and through contributions to the OpenStreetMap community.

• Leaflet (Intermediate): Used in the GIS Cadastral System to handle spatial data efficiently.

• Mapbox GL JS (Advanced): Implemented for a real-time vehicle tracking platform to monitor live locations and optimize logistics.

GIS Experience

• He has designed and implemented ETL pipelines, developed APIs for geospatial data workflows, and created interactive dashboards and visualization tools for companies like Development Seed. His project work includes a cadastral management system, a real-time vehicle tracking platform, and an arenavirus prediction platform (with a custom filtering and visualization system for virus distribution insights).

Highly Relevant Industry Experience

• Transportation Systems: Architected a real-time vehicle tracking platform using an ETL process with Kafka, Python, Docker, and AWS Lambda. The system included an admin dashboard for monitoring vehicle movements and optimizing logistics.
• Risk Management: Developed a platform for mitigating risks in the sustainable development of natural rubber, utilizing ReactJS, Python, and Mapbox (See project here)

SQL Familiarity

• Familiar with SQL and experienced with PostgreSQL/PostGIS, having worked as a data engineer designing and integrating spatial databases for data-driven applications.

Data Visualization

• Experience building interactive data visualization tools and dashboards using libraries like Mapbox and D3.js.

Education

• Bachelor of Science in Systems Engineering | Universidad Nacional de San Cristóbal de Huamanga (2018)

Certifications

• Holds self-taught certifications in AWS, GCP, PostgreSQL, Python, and GIS.

Key Qualifications & Highlights

• Full-Stack GIS Architecture: Possesses a comprehensive understanding of the entire data pipeline. He designs and builds end-to-end solutions, starting with raw data processing in , , and , creating microservices with or , and rendering the data on the frontend with and libraries such as OpenLayers or Mapbox.with raw data processing in Python, GDAL, and PostGIS, creating microservices with Django or FastAPI, and rendering the data on the frontend with React and libraries such as

• Real-Time Data Processing: Experience engineering high-throughput, real-time data systems. For a vehicle tracking platform, he implemented an ETL process using Kafka and WebSockets to manage live location data from ingestion to visualization, demonstrating expertise in event-driven architecture.

• Backend-Aware Frontend Development: Uniquely skilled at bridging the gap between backend data processing and frontend implementation. He frequently uses Python for backend tasks to ensure the frontend receives perfectly structured geospatial data, creating a seamless and efficient development workflow.

• Open Source Contributor: Actively contributes to the OpenStreetMap community, utilizing Java and OpenLayers. This demonstrates a deep understanding of core open-source geospatial technologies and a commitment to the field.

Screening Interview - Key Observations

• GIS Tooling Rationale: Articulated a clear distinction between GIS libraries, stating he prefers Mapbox for commercial projects due to its managed performance, while using OpenLayers primarily for government projects or open-source contributions (like OpenStreetMap) where free tooling is a requirement. He mentioned handling performance by controlling vector rendering based on zoom levels.

• Core Strength - Backend-Driven Frontend: Explicitly identified his strongest skills as Python (7 years) for backend processes and React (5 years) for the frontend. He described his typical workflow as processing raw geospatial data with Python and GDAL, storing it in a PostGIS database, building a Python microservice, and serving structured data to the React front end.

• Stated Challenge - UI/UX Ambiguity: When asked about his least enjoyed tasks, he identified the frontend as more difficult, not due to the technology, but because of the need to negotiate with designers and make UI decisions when designs are incomplete. He prefers the concrete logic of backend development.

• Real-Time Architecture - Vehicle Tracking: Described architecting a real-time vehicle tracking platform where he implemented an end-to-end data pipeline using Kafka to manage and stream live location data from ingestion to the frontend.

• Location & Availability: Currently traveling in Europe for English language studies. Although he confirmed attendance at the 11 AM EST stand-up, he has limited availability before then due to his study schedule. He expressed a preference for working at night.