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San Francisco, California, United States
Remote only
The challenge: Every minute matters in wildfire response. As climate change increases the frequency and intensity of wildfires—with longer fire seasons, drier fuels, and more extreme weather—new ignitions can spread rapidly, putting communities, critical infrastructure, and ecosystems at risk. Today, many wildfires are first reported by members of the public, meaning it can take valuable time to detect a fire, confirm its location and size, and mobilize responders. Fire agencies need faster, more reliable ways to detect, verify, and pinpoint new ignitions so they can respond quickly and prevent small fires from becoming catastrophic events.
About Pano AI: Pano AI is the leader in AI-powered wildfire detection and intelligence, helping fire professionals detect, respond to, and contain wildfires faster and more safely. Our platform combines advanced hardware, software, artificial intelligence, satellite imagery, and other data sources to provide real-time situational awareness and actionable intelligence. Using a network of ultra-high-definition, 360-degree cameras positioned across high vantage points, Pano AI delivers a real-time view of wildfire activity, enabling faster, more informed decision-making when every second counts.
We are a team of more than 175 people working in a hybrid-remote environment across North America and Australia, with headquarters in San Francisco. Our customers include government agencies, utilities, insurers, and private landowners who rely on Pano AI to help protect people, property, and natural landscapes. Pano AI currently serves customers across the United States, Australia, and Canada, monitoring more than 50 million acres worldwide.
Our values are part of everything we do at Pano AI. They guide how we work together, how we serve our customers, and how we approach our mission.
Impact: As we scale our business, we grow our impact—enabling emergency managers to protect people, infrastructure, and the environment from devastating wildfires.
Service: We serve those who serve, and the teammates beside us.
Trust: We earn trust through integrity, accountability, and an obsession with quality so that our partners can rely on us.
Excellence and Speed: We produce exceptional work quickly because our mission demands both precision and urgency.
Innovation: We apply cutting-edge technology to what we build and how we work.
Our work has been recognized by Fast Company as one of the Top 10 Most Innovative AI Companies in 2023 and one of the World's Most Innovative Companies in 2026, ranking #1 in Sustainability. We have also been named to TIME's list of the 100 Most Influential Companies of 2025 and recognized by MIT Technology Review as one of the top climate technology companies to watch.
Backed by $89 million in funding from leading investors including Giant Ventures, Liberty Mutual Ventures, Tokio Marine Future Fund, Congruent Ventures, Initialized Capital, Salesforce Ventures, and T-Mobile Ventures, we're building technology that helps communities around the world become more resilient to wildfire. Learn more at www.pano.ai.
Pano AI seeks an Analytics Engineer to bring dedicated data and analytics support to our Ops team, during a period of rapid growth in our human review operations. You will be a technically strong, curious professional who is as comfortable digging into a messy SQL query as you are sitting with an agent to understand how they actually work a case — and who takes pride in turning ad hoc investigation into durable, trustworthy tooling.
As Pano scales its wildfire detection network and brings on additional review vendors, the volume and complexity of the data Ops depends on is growing quickly. This role will give Ops the dedicated analytics capacity it needs to catch data quality issues early, keep vendor performance reporting accurate as operations evolve, and build the forecasting tools that keep staffing ahead of demand.
You will own the dbt models, SQL-powered Metabase dashboards, and tools that both individual contributors and leadership use every day as real operational infrastructure. You will work with our existing Analytics stack to implement end-to-end solutions consisting of importing data from a third party, modeling that data through dbt, and building Metabase dashboards using SQL. This is a role for someone who wants to live in the team's real-world processes — understanding how Ops actually operates day to day — rather than one who only interacts with the business from behind a query editor.
Own the dbt models, Metabase dashboards, and self-serve tools that ICs and leadership rely on daily to do their jobs and check KPIs
Investigate performance differences across vendors and time periods to surface trends and gaps that inform operational decisions
Catch data quality issues in core reporting tables before they mislead decisions, and implement a fix whether that fix requires a change to underlying dbt models the tooling built on tope of those models
Perform root-cause analysis of anomalies in vendor and agent performance data using a rigorous, hypothesis-driven approach
Keep metric definitions consistent as Ops scales across multiple vendors, and proactively catch broken or misleading metrics before they reach a decision-maker
Forecast staffing needs by analyzing historical incident volume, seasonality, and throughput data — and by identifying and incorporating external data sources (e.g., fire activity, weather, seasonal trends) where they meaningfully improve the forecast
Analyze headcount and coverage against demand, and support scenario planning for peak wildfire season staffing
Document findings clearly for both technical and non-technical audiences
Spend real time with Ops' day-to-day workflows — understanding how agents and leads actually work — and bring that context back into the tools and analysis you build, rather than working solely from the back end
Travel to international vendor sites to observe operations firsthand and bring those insights back into your analysis and tooling
Proven technical proficiency in SQL, including the ability to manage and sustain production-grade BI infrastructure across platforms like Metabase or Looker
Experience using dbt (Cloud or Core) to develop robust data models complete with unit tests and documentation
An inquisitive mindset with a commitment to immersing yourself in operational workflows, moving beyond the query editor to understand the human side of the process
Proficiency in Python for statistical analysis; hands-on experience with time-series modeling or predictive forecasting is highly valued
Ability to identify and integrate relevant third-party datasets to enhance internal analysis and provide a more comprehensive operational picture
A rigorous, skeptical approach to data, ensuring you deeply understand what a metric represents before utilizing it for critical decisions
Effective collaboration skills for partnering with central data teams, including the ability to define well-scoped and prioritized requests
Articulate written communication capable of translating complex analytical findings for both technical peers and operational stakeholders
Resilience and adaptability within a high-stakes, rapid-growth environment where data integrity is paramount to operational success
Final compensation for regular 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, regular full-time roles are eligible for equity. Benefits are tailored to local market standards and statutory requirements in the employee's country of employment, and may include health coverage, retirement or pension contributions, and paid time off. Specific benefit details will be shared during the interview process.
