AI Analytics Engineer (Business Analytics)
Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done.
What this role actually needs.
Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done. Responsibilities: - Design and maintain trusted data models for core financial metrics including ACV, ARR, billings, revenue recognition, and cost allocation, managing the full lifecycle from prototyping through production - Develop and govern dbt data pipelines , establishing data integrity standards and SLAs that ensure timely and accurate delivery of financial data across Finance and Accounting - Build and optimize dashboards that deliver real-time, self-serve insights across key financial areas including revenue performance, expense tracking, budget variance, and forecasting accuracy - Enable data independence for Finance stakeholders by eliminating reliance on ad-hoc data requests and manual reporting, building scalable self-service datasets in Looker and Omni for the Finance team and broader company - Collaborate with Finance and data partners to establish the AI Business Context layer for financial use cases, translating accounting logic, metric definitions, and business rules into structured formats that AI tools can interpret accurately - Develop tools that enable natural language access to financial data and AI-assisted reporting , empowering non-technical Finance stakeholders to explore insights independently Requirements: - Not married to legacy tooling: You’re more interested in what’s emerging than what’s established. You evaluate tools based on what they enable, not how long they’ve been around. Company context: Airtable builds collaborative software, AI-enhanced workflows, and enterprise platform tooling for modern operations teams.
Day-to-day expectations
Airtable lists these responsibilities for the AI Analytics Engineer (Business Analytics) role.
- Design and maintain trusted data models for core financial metrics including ACV, ARR, billings, revenue recognition, and cost allocation, managing the full lifecycle from prototyping through production
- Develop and govern dbt data pipelines , establishing data integrity standards and SLAs that ensure timely and accurate delivery of financial data across Finance and Accounting
- Build and optimize dashboards that deliver real-time, self-serve insights across key financial areas including revenue performance, expense tracking, budget variance, and forecasting accuracy
- Enable data independence for Finance stakeholders by eliminating reliance on ad-hoc data requests and manual reporting, building scalable self-service datasets in Looker and Omni for the Finance team and broader company
- Collaborate with Finance and data partners to establish the AI Business Context layer for financial use cases, translating accounting logic, metric definitions, and business rules into structured formats that AI tools can interpret accurately
- Develop tools that enable natural language access to financial data and AI-assisted reporting , empowering non-technical Finance stakeholders to explore insights independently
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Not married to legacy tooling: You’re more interested in what’s emerging than what’s established. You evaluate tools based on what they enable, not how long they’ve been around.
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Source: job-boards.greenhouse.io · Source ID: 8470036002 · Confidence: 91/100 · Last checked: May 7, 2026
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