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# Geospatial Data Engineer

- **Company:** PANO AI, INC.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $111,000.0 - $144,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Geographic Information Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, ArcGIS (Software), BigQuery, Cloud Computing, Code Review, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Spatial Databases, Python (Programming Language), PostgreSQL, PostGIS, Quantum GIS (QGIS), Standard Sql, Salesforce.Com, Software Construction, Software Engineering, Spatial Data Infrastructures, SQL Databases, Data Ingestion, Snowflake, Git Flow, Information Technology, Geospatial Data Abstraction Library (GDAL), Software Version Control, Data Pipelines
- **Published:** May 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9fd1429db835fa7a

## About the Role

Do you have experience in Version control systems?, * 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

Nice to have

 * 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

## Description

As a Geospatial Data Engineer on the Geospatial Analytics team, you will design, build, and maintain the data infrastructure that powers Pano AI's geospatial analytics workflows-from ingestion pipelines and spatial databases to automated processing systems and internal tooling. This role works closely with geospatial data analysts, data scientists, and cross-functional partners in Sales, Operations, Product, and Engineering to ensure that spatial data is reliable, scalable, and readily accessible. An ideal candidate brings 2-4 years of experience in data or software engineering, solid command of Python and SQL, and hands-on familiarity with geospatial data formats and spatial databases. You will contribute to the full data lifecycle, writing clean and well-tested code, participating in code reviews, and helping establish engineering standards on a growing team.

What you'll do

 * 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


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