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About the role

This role sits at the intersection of data engineering and analytics to support wildfire operations and public safety. You will own the flow of data from remote sensors and satellite feeds into decision-ready insights for field teams and government agencies. The position requires building and maintaining the pipelines that ensure imagery and telemetry are reliable and timely. You will work directly with analysts and incident commanders to translate operational needs into data products. Success in this role means faster detection-to-action windows and more informed decisions during evolving fire events. You will collaborate across distributed teams in North America and Australia to maintain standards that satisfy public agency requirements. This is a hands-on role where your work directly supports communities in difficult weather and remote environments.

Key facts

What you'll do

Design and maintain data pipelines that convert satellite and imagery feeds into operational intelligence for fire response.

Implement edge AI solutions that function reliably in remote or harsh environments to support continuous monitoring and alerts.

Transform raw sensor and weather data into metrics that reduce detection-to-action windows for responders.

Partner with analysts to validate data quality and ensure faster validation supports timely decisions during evolving incidents.

Develop dashboards and analysis that help landowners and insurers manage risk in difficult weather and changing conditions.

Coordinate with distributed teams across North America and Australia to align workflows with regional regulations and operational needs.

Support the deployment of models and data products that ensure operations continue even when connectivity is constrained in the field.

Translate evolving agency requirements into data system improvements that keep safety standards and reporting current.

Enable communities and agencies to receive timely updates that accurately reflect current fire behavior and risk.

Contribute to a strong portfolio of analyses and data products that demonstrate impact more than formal degrees in hiring decisions.

Champion best practices in data reliability, security, and documentation for public-sector clients and government workflows.

Explore new methods for turning imagery and telemetry into clear operational insights that drive strategy and resource planning.

Requirements

A Bachelor's degree in a relevant field is required for AI and IoT work in wildfire operations. The degree requirement ensures foundational knowledge for analytics, modeling, and data pipelines.

Edge AI solutions must operate reliably in remote or harsh environments for utility and government clients. Reliability in these settings supports continuous monitoring and timely alerts when connectivity is constrained.

Satellite and imagery data transform into operational insights for fire response and risk modeling for agencies and landowners. Analysts and engineers use these insights to inform decisions and strategy.

Collaboration occurs across distributed teams in North America and Australia while adhering to data and safety standards for public agencies. Teams align workflows and outputs with regional regulations and operational needs.

A strong portfolio of past analyses matters more than degrees in many hiring decisions.

You are comfortable working with SQL and scripting to manage data pipelines and prepare datasets for analysis.

You can interpret statistics and communicate uncertainty clearly to both technical and non-technical stakeholders.

Experience with data visualization, modeling, or infrastructure is expected given the complexity of wildfire operations.

You understand the importance of reliability, documentation, and repeatable processes in systems that support public safety.

You are able to work full time in San Francisco, California, and navigate the practical constraints of the role.

Practical notes

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

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.

About the company

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.