Help us tackle the growing wildfire crisis with the latest advancements in AI and IoT

Who we are

The problem: Every minute matters in fire response. As climate change amplifies the intensity of wildfires—with longer fire seasons, dryer fuels, and faster winds—new ignitions spread faster and put more communities at risk. Today, most wildfires are detected by bystanders and reported via 911, meaning it can take hours to detect a fire, verify its exact location and size, and dispatch first responders. Fire authorities need a faster way to detect, confirm, and pinpoint fires so that they can quickly respond—preventing small flare-ups from becoming devastating infernos.

About Pano: We are a 175+ person growth-stage hybrid-remote start-up, headquartered in San Francisco. We are the leader in early wildfire detection and intelligence, helping fire professionals respond to fires faster and more safely—with the right equipment, timely information, and enhanced coordination—so that they can stop a new ignition before it grows. Pano AI combines advanced hardware, software, and artificial intelligence into an easy-to-use, web-based platform. Leveraging a network of ultra-high-definition, 360-degree cameras atop high vantage points, as well as satellite and other data feeds, Pano AI produces a real-time picture of threats in a geographic region and delivers immediate, actionable intelligence.

Pano AI has been recognized by Fast Company as one of the Top 10 Most Innovative AI Companies in 2023, and as one of the Top 50 Most Innovative Companies of 2026—ranking #1 in the Sustainability category. The company was also named to TIME’s list of the 100 Most Influential Companies of 2025 and identified by MIT Technology Review as one of the top 15 climate tech companies to watch in 2024. Pano AI has been featured in leading publications, including The Wall Street Journal, Bloomberg, and CNBC.

The company serves dozens of government and enterprise customers across 16 U.S. states, five Australian states, and British Columbia, Canada, and currently monitors more than 50 million acres of land worldwide. It has raised $89 million in venture capital from investors including Giant Ventures, Liberty Mutual Ventures, Tokio Marine Future Fund, Congruent Ventures, Initialized Capital, Salesforce Ventures, and T-Mobile Ventures. Learn more at https://www.pano.ai/.

The Role

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

What you’ll bring

• 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

Final compensation for full-time employees is determined by a variety of factors, including job-related qualifications, education, experience, skills, knowledge, and geographic location. In addition to base salary, full-time roles are eligible for stock options. Our benefits package also includes comprehensive medical, dental, and vision coverage, a matching 401(k) plan, and flexible paid time off.