AI Analytics Engineer (AI & Analytics Platform)

We're looking for an AI Analytics Engineer to help define what AI-powered analytics looks like from the ground up. As one of the first hires shaping this discipline, you'll build the systems that make AI tools accurate, design the workflows that make them trustworthy, and partner across the business to drive adoption.

Key Responsibilities:

• Build and maintain context infrastructure: Translate institutional business knowledge into structured formats so that AI tools can answer questions accurately.
• Design and run evaluation frameworks: Develop predefined test cases, accuracy benchmarks, and validation workflows that measure whether AI-generated insights are trustworthy.
• Build and orchestrate AI agent systems: Help design, build, and iterate on the agent architectures that power our analytics tools.
• Experiment and evaluate: Test prompt configurations, agent behaviours, and model outputs across different use cases.
• Develop internal AI tooling and workflows: Build tools and automations that improve DS&A's own efficiency.
• Build automated insight generation systems: Design and develop AI-powered systems that proactively surface patterns, anomalies, and meaningful changes in business data.
• Drive cross-functional adoption: Partner with GTM, Product, Finance, and other teams to onboard users, field questions, triage issues, and train stakeholders on how to get the most out of our AI-powered analytics tools.

Requirements:

• Strong SQL proficiency and experience working with modern data tools (dbt, Databricks, Snowflake, or similar).
• Clear, structured writing: Can translate complex business logic into documentation that both humans and LLMs can interpret.
• Hands-on experience with AI tools (Claude, ChatGPT, Cursor, or similar) beyond casual use.
• Cross-functional communication: Can partner with non-technical stakeholders to understand needs, triage issues, and drive adoption.
• Builder mindset: Comfortable picking up new technical skills, prototyping solutions, and iterating quickly.

Nice to Have:

• Experience with BI semantic modeling (Looker, Omni Analytics, or similar).
• Familiarity with Python and LLM APIs.
• Experience building evaluation or testing frameworks.
• Background in context engineering, knowledge management, or technical writing.
• Experience with agent architectures, prompt engineering, or AI system design.
• Familiarity with data science and ML concepts (e.g., experimentation, time series analysis, statistical modeling, clustering, anomaly detection).

Airtable is an equal opportunity employer. We embrace diversity and strive to create a workplace where everyone has an equal opportunity to thrive.