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Analytics Engineer-Operations

Pano AI LogoPano AI
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Salary

$110,000 - $140,000

Location

US - Remote

Posted

Yesterday

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.

What you'll do

  • 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

What you'll bring

  • 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

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