AI Analytics Engineer (marketing Analytics)

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

Airtable is seeking an AI Analytics Engineer to embed within their Marketing organization. The role involves building canonical data infrastructure, owning dashboards, and enabling data-driven decisions. The candidate must be genuinely AI-native, integrating AI tools into their daily workflow for tasks like writing SQL and debugging. Responsibilities include designing data models, developing dbt pipelines, building dashboards, and leading the development of tools for natural language data access and AI-assisted reporting. Requires expert SQL, dbt, BI platforms, and active daily use of AI coding tools.

What you'd actually do

  1. Design and maintain trustworthy data models for core marketing metrics, managing the full lifecycle from prototyping through production.
  2. Develop and govern dbt data pipelines, establishing data integrity standards and SLAs for timely, accurate delivery across the Marketing organization.
  3. Build and optimize dashboards that deliver real-time, self-serve insights across high-priority marketing areas: campaign performance, funnel conversion, pipeline contribution, and lead scoring.
  4. Lead the development of tools that facilitate natural language data access and AI-assisted reporting for non-technical stakeholders.
  5. Translate complex data insights into clear business recommendations via dashboards, memos, and presentations.

Skills

Required

  • Expert-level SQL
  • Proficiency with dbt or equivalent data transformation tools
  • Experience with BI and visualization platforms (Looker, Omni, Tableau, Hex, or similar)
  • Active, demonstrated daily use of AI coding tools (Cursor, Claude, ChatGPT, Gemini)
  • Mandatory use of GitHub for version control in a standard development workflow
  • Exceptional communication skills

Nice to have

  • Python for data work (pandas, ETL scripting, or analysis)
  • Prior exposure to marketing data concepts: attribution, funnel metrics, lead scoring, or campaign performance
  • Familiarity with CRM (Salesforce) or marketing automation platforms (Marketo)
  • Experience with Databricks or cloud data warehouses
  • A public portfolio showcasing data or AI-assisted engineering work (GitHub, personal projects, Kaggle)

What the JD emphasized

  • genuinely AI-native professional
  • integrates them as a core part of their daily workflow
  • Active, demonstrated daily use of AI coding tools
  • Candidates must provide specific, concrete examples of how these tools are integral to their work, moving beyond simple familiarity.

Other signals

  • AI-native professional
  • integrates AI tools as a core part of their daily workflow
  • AI-assisted reporting
  • AI coding tools