Geospatial Data Engineer

Alternate titles: Geospatial Data Engineer | GIS Application Engineer

Position Summary

The Data, Analytics, & Insights organization is an enterprise team focused on unlocking the value of data to support a comprehensive Wildfire Safety Program. In this role, you will collaborate with cross-functional product teams, including data scientists and solution architects, to deliver mission-critical data pipelines and asset risk models. You will build pipelines that aggregate grid asset health data, network topology, and complex geospatial features on core enterprise data platforms.

Core Responsibilities

• Data Pipeline Construction: Build high-performance, automated data pipelines in AWS and other cloud environments using PySpark and Python to ingest large-scale utility asset and outage data.
• Palantir Foundry Development: Support and build automated end-to-end data workflows, code repositories, and transformations within the Palantir Foundry ecosystem.
• Distributed Spatial Processing: Implement advanced geospatial data processing and performance optimizations using big-data spatial libraries such as Apache Sedona, GeoPandas, and Shapely.
• Safety and Grid Integration: Integrate with systems supporting remote inspections, Public Safety Power Shutoffs (PSPS), LiDAR-driven vegetation management, and asset risk modeling.
• Agile Collaboration: Actively participate in Agile/Scrum ceremonies and apply software engineering practices including unit testing, CI/CD, and disciplined source control.

Required Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, GIS, or a related quantitative field.
• Experience: 5+ years of experience in data engineering and ETL ecosystems.
• Programming and Data: Proficiency in PySpark, Python, SQL, and Apache Sedona.
• Coordinate Reference Systems: Understanding of EPSG codes and common datum considerations, including NAD83 and WGS84.
• Spatial Data Formats: Knowledge of Shapefile, GeoJSON, GeoParquet, GeoPackage, KML, and raster formats such as GeoTIFF and Cloud Optimized GeoTIFF (COG).
• Spatial Indexing and Partitioning: Experience with spatial joins, R-trees, grid or quadtree indexing, and broadcast versus partitioned joins in Apache Sedona.
• Geometry Operations at Scale: Experience with buffers, intersections, nearest-neighbor analysis, topology validation, simplification, and remediation of invalid or self-intersecting geometries.
• Orchestration: Experience with Airflow, Dagster, or a comparable platform-native orchestration capability.
• Data Modeling and Warehousing: Knowledge of dimensional modeling, slowly changing dimensions, and strategies for managing large historical datasets.

Preferred Qualifications

• Enterprise Data Platforms: Experience working in Palantir Foundry or an equivalent cloud-based big-data platform.
• Spatial Big Data: Experience working with complex database structures, network topology, or spatial analytics frameworks such as Apache Sedona or GeoSpark to process large-scale geometric datasets.
• Industry Data: Experience with utility asset, outage, inspection, vegetation management, LiDAR, or risk-modeling data is strongly preferred.