GeoSpatial Data Engineer

Enexus Global Inc.; Oakland (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Enexus Global Inc. is seeking a seasoned Data, Analytics, & Insights professional to lead enterprise data pipelines and asset risk modeling for wildfire safety initiatives in Oakland, CA.

You will collaborate with data scientists and solution architects to deliver scalable data platforms and robust geospatial capabilities. You will develop end-to-end Palantir Foundry workflows, optimize spatial processing, and ensure robust integration with PSPS, LiDAR vegetation planning, and remote

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 NAD83/WGS84.
• Spatial Data Formats: Knowledge of Shapefile, GeoJSON, GeoParquet, GeoPackage, KML, GeoTIFF, and COG.
• Spatial Indexing and Partitioning: Experience with spatial joins, R-trees, grid or quadtree indexing, and Sedona joins.
• Geometry Operations at Scale: Buffers, intersections, nearest-neighbor analysis, topology validation, remediation of invalid geometries.
• Orchestration: Experience with Airflow, Dagster, or equivalent.
• Data Modeling and Warehousing: Knowledge of dimensional modeling and managing large historical datasets.

Responsibilities

• Data Pipeline Construction: Build high-performance, automated data pipelines in AWS/PySpark/Python to ingest large-scale asset/outage data.
• Palantir Foundry Development: Support and build automated end-to-end data workflows and transformations within Foundry.
• Distributed Spatial Processing: Implement geospatial processing with Sedona, GeoPandas, Shapely.
• Safety and Grid Integration: Integrate with systems for remote inspections, PSPS, LiDAR vegetation management, and risk modeling.
• Agile Collaboration: Participate in Agile/Scrum ceremonies and apply CI/CD and source control practices.

Skills

Geospatial data processing

Big data engineering

ETL pipelines

Data orchestration

Coordinate reference systems

Spatial data formats

Education

Bachelor's degree in Computer Science, Engineering, GIS, or a related quantitative field

Tools

Palantir Foundry

Airflow

Dagster

Job description

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.