Geospatial Data Engineer

must be a local candidates based in California.
I'm looking for an experienced Data Engineer with a strong geospatial background and expertise in AWS, Apache Sedona/GeoPandas, and PySpark.
Location: California (100% Remote)
Core Responsibilities
Data Pipeline Engineering
Design, develop, and maintain scalable AWS-native data pipelines using Python and PySpark.
Build automated ingestion, transformation, and processing workflows for large geospatial and operational datasets.
Optimize pipeline performance, scalability, and reliability.
Remote Sensing & Raster Data Processing
Develop and support data pipelines for large raster-based datasets and satellite imagery.
Manage multi-band imagery, including visible, near-infrared, and red-edge spectral bands.
Implement selective and incremental ingestion strategies to efficiently process only relevant data subsets.
Cloud Data Architecture
Design and maintain cloud-native geospatial data solutions within AWS.
Support data lake, warehouse, and analytical platform initiatives.
Geospatial Processing
Develop distributed geospatial processing solutions using Apache Sedona, GeoPandas, Shapely, and related technologies.
Perform large-scale spatial analysis, joins, indexing, and optimization.
Data Platform Development
Support modern data engineering practices including CI/CD, automated testing, version control, and Infrastructure as Code.
Contribute to enterprise data governance and data quality initiatives.
Agile Collaboration
Work closely with Business Analysts, Product Owners, GIS Specialists, Data Scientists, and Engineering teams in an Agile environment.
Participate in sprint planning, design reviews, and technical discussions.
Required Qualifications
Education
Bachelor's degree in Computer Science, Engineering, GIS, Geography, Data Science, or a related field.
Experience
7+ years of Data Engineering experience designing and supporting enterprise-scale data pipelines.
Technical Requirements
Strong proficiency with Python, PySpark, SQL, and Apache Sedona.
Hands-on experience building geospatial and raster data processing solutions on AWS.
Experience developing cloud-native ETL and data integration workflows.
Strong understanding of coordinate reference systems, projections, and spatial transformations (WGS84, NAD83, EPSG standards).
Geospatial Technologies
Experience with Shapefile, GeoJSON, GeoParquet, GeoPackage, KML, GeoTIFF, and Cloud-Optimized GeoTIFF (COG).
Experience processing large-scale raster datasets and satellite imagery.
Expertise with raster-vector analysis and multi-band imagery processing.
Understanding of vegetation, environmental, and remote sensing analytics workflows.
Spatial Analytics
Spatial indexing and partitioning techniques including R-Tree, QuadTree, and distributed spatial joins.
Geometry operations including buffering, intersections, nearest-neighbor analysis, topology validation, and geometry simplification.
Experience addressing performance optimization challenges for large spatial datasets.
Data Engineering & Orchestration
Experience with Airflow, Dagster orchestration platforms.
Knowledge of dimensional modeling, historical data management, and data warehousing concepts.
Familiarity with CI/CD practices, automated testing, and Git-based development workflows.
Nice to Have
Experience with Palantir Foundry.
STAC or other satellite imagery cataloging standards.
LiDAR datasets and processing workflows.
Utility, energy, environmental, infrastructure, or asset management industry experience.
Experience building geospatial machine learning or advanced analytics solutions.

Work arrangement
Yes