Geospatial Data Engineer

Pano AI is a growth-stage hybrid-remote start-up focused on early wildfire detection and intelligence. The Geospatial Data Engineer will design, build, and maintain the data infrastructure for geospatial analytics, collaborating with various teams to ensure data reliability and scalability.

Responsibilities:

• Design, build, and maintain scalable data pipelines that ingest, transform, and load geospatial datasets to support efficient and scalable geospatial analytics
• Develop and optimize PostGIS and PostgreSQL database schemas to support geospatial analytics, viewshed computations, and site selection workflows
• Write and maintain Python-based automation scripts and geospatial processing tools, following software engineering best practices including code reviews, pull requests, and version control with Git/GitHub
• Collaborate with geospatial data analysts and scientists to understand data requirements and translate them into reliable, well-documented engineering solutions
• Monitor and maintain data quality, pipeline reliability, and system performance for production geospatial data products
• Support integration of geospatial data infrastructure with internal dashboards, APIs, and product engineering systems
• Support analytics workflows as needed
• Contribute to special projects and cross-functional initiatives as the team’s data infrastructure needs evolve

Requirements:

• Bachelor's degree in Computer Science, Engineering, Geography, Statistics, Math, or a related field
• 2–4 years of experience in data engineering, software engineering, or a closely related role
• Proficiency in Python, including experience writing modular, tested, and maintainable code using geospatial Python libraries such as GeoPandas, Shapely, Rasterio, or GDAL
• Solid SQL skills and hands-on experience with PostgreSQL and PostGIS for querying and managing spatial data
• Fluency with Git/GitHub workflows, including branching strategies, pull requests, and code reviews
• Working knowledge of geospatial data formats (GeoJSON, GeoTIFF, Shapefile, etc.) and coordinate reference systems
• Experience building or maintaining ETL or data pipeline workflows in a production environment
• Strong communication skills and ability to work collaboratively across technical and non-technical stakeholders
• Experience with ArcGIS Pro or QGIS highly preferred
• Experience with cloud-based geospatial platforms or data warehouses (e.g., BigQuery, Snowflake, AWS, or GCP)
• Experience with Salesforce integrations
• Experience with ArcGIS Online and ArcGIS Enterprise
• Experience with workflow orchestration tools such as Temporal, Airflow, Prefect, or similar