Principal Geospatial Data Engineer (Algorithms & Datasets)

Data Scientist – Geo Spatial Data Sets 

AWS and Google Cloud Platform 

Special Data Science with Algorithm AND Data Sets 

ArcGIS Framework and ESRI 

Risk based Applications – Rest API, Django. 

Python experience 

Cross Functional Partnership 

Building Algorithm – This is one of the key skills 

5+ years of experience in Data Engineering, Geospatial Analytics, Or Data Science roles, with demonstrated leadership in technical delivery. 

Core Languages & Web Frameworks: Proficiency in Python (3.x), SQL, and microservices/web execution frameworks (Flask, FastAPI, or Django) a must. 

Spatial & GIS Mastery: Deep expertise with spatial analytics tools, spatial SQL, PostGIS, ESRI/ArcGIS frameworks, and open-source mapping platforms (e.g., Overture Maps, QGIS, PG Tile Server). 

Design, build, and optimize spatial routing and network planning models across our core automated planning platforms and fiber mapping engines. 

Drive the strategic of software convergence legacy spatial planning scripts into a unified, high-performance network modeling stack. 

Execute large-scale Feeder Distribution Hub (FDH) and Fiber-to-the-Home (FTTH) remodeling using Djikstra, Kruskal or other minium tree spanning algorithms utilizing network topology, and spatial datasets. 

Transition and standardize spatial graph processing and routing engines 

Ability to handle large datasets in spatial data formats (e.g., shapefiles, GeoJSON) 

Integrate advanced spatial enterprise address databases, and master location repositories) into automated ETL pipelines for high- accuracy routing and spatial analysis. 

Oversee production relational and columnar databases across cloud environments. 

Lead continuous database optimization initiatives— automating table maintenance, rebuilding high-traffic spatial reference cross-reference tables, and tuning long-running queries to maintain low latency. 

Build and manage automated workflow pipelines (e.g. Airflow/Python) to streamline real-time data sharing across cross-functional engineering, analytics, and AI teams. 

Architect secure, scalable, and cost-effective data infrastructure environments across multi-cloud environments (AWS and Google Cloud Platform).