AI Analytics Engineer (business Analytics)

Airtable Airtable · Enterprise · Austin, New York, San Francisco · Data

Airtable is seeking an AI Analytics Engineer to build the next generation of analytics infrastructure and tooling with an AI-first approach. The role will own financial data models and analytics infrastructure, build scalable data pipelines, dashboards, and AI-powered systems to enable self-serve insights. Key responsibilities include developing trusted data models, dbt pipelines, dashboards, enabling data independence for Finance stakeholders, establishing an AI Business Context layer, developing tools for natural language access to financial data and AI-assisted reporting, and designing AI-powered workflows to surface patterns and anomalies. The role also involves serving as a primary data partner for FP&A, Accounting, and Finance leadership, driving adoption of analytics and AI tooling, and developing expertise in Airtable's financial data models and BI tools.

What you'd actually do

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

Skills

Required

  • SQL proficiency
  • experience with modern data tools (dbt, Databricks, Snowflake, or similar)
  • strong intuition for data validation and troubleshooting
  • clear communication and strong writing skills
  • business acumen and curiosity
  • ability to thrive in ambiguity and own workstreams end-to-end
  • technical curiosity and eagerness to experiment with AI tooling (LLMs, prompt engineering)
  • bias toward building and making things

Nice to have

  • experience with Looker
  • experience with Omni

What the JD emphasized

  • AI-first approach
  • AI-powered systems
  • AI Business Context layer
  • AI-assisted reporting
  • AI-powered workflows
  • AI tooling

Other signals

  • AI-first approach to building analytics infrastructure
  • AI-powered systems for Finance stakeholders
  • AI Business Context layer for financial use cases
  • natural language access to financial data and AI-assisted reporting
  • AI-powered workflows that automatically surface patterns, anomalies, and meaningful changes in financial data
  • cross-functional adoption of analytics and AI tooling