Business Intelligence Analyst

Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.

Agility Robotics is scaling its Operations Business Intelligence function. As a Business Intelligence Analyst, you will help turn operations data into analytics that people trust so the organization runs on numbers instead of anecdotes.

You'll join a small fast moving BI team embedded within Operations and report to the Business Intelligence Manager. This is a full-stack role: you'll gather requirements directly from stakeholders across Operations; build and maintain governed dashboards and semantic models; and, as we bring Databricks and dbt online, help build the Operations specific data models that sit on top of our data platform's governed layers. The BI team owns requirements, metric definitions, and the analytics layer that turns raw tables into numbers people can trust.

This role is a strong fit for someone who can move fluidly between a stakeholder conversation and a SQL window function and who wants to help shape how a data function gets built rather than just execute inside one that already exists.

• Partner directly with stakeholders across Operations, Quality, Production, and Supply Chain to gather requirements and translate open-ended business questions into structured, testable data questions.

• Design, build, and maintain dashboards, reports and semantic models that give teams a consistent, governed view of performance.

• Write and optimize SQL - CTEs, window functions, aggregations - against production and unstructured data sources to build reports and independently validate findings.

• Help lead transition from Power BI based reporting onto Databricks/dbt by building the Operations specific dbt models that sit on top of data engineering's governed layers to answer Operations specific questions.

• Contribute to metric definitions and governance: work with the people accountable for a number until there's one definition, one source, one owner.

• Validate your own analysis before it goes wide: know the failure modes of your data, spot-check outputs against a second source, and flag data-quality issues rather than assuming a query or pipeline is right by default.

• Maintain and improve the automated data extracts that teams across Operations rely on daily.

• Use AI tools to accelerate your own SQL, dbt, and dashboard development with the judgment to verify outputs rather than trust them blindly.

• Document your models, dashboards, and logic clearly enough that a teammate can maintain them without you in the room.

• Other duties as assigned.

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