AI Analytics Engineer (AI & Analytics Platform)

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. Airtable is building the infrastructure that makes AI-powered analytics trustworthy and scalable — and we're looking for an AI Analytics Engineer to help define what that looks like from the ground up. This is a new role on a new team. Our Data Science Analytics org is standing up an AI Analytics Platform function to own the context layer, evaluation frameworks, and adoption strategy behind our internal AI analytics tools — including our natural-language-to-SQL capabilities, Claude, and Omni Analytics. The goal: shift from a world where analysts are the bottleneck for every data question to one where the organization can self-serve with confidence. You'll be one of the first hires shaping this discipline. That means you won't just use AI tools — you'll build the systems that make them accurate, design the workflows that make them trustworthy, and partner across the business to drive adoption. If you're excited about working at the intersection of data engineering, LLM tooling, and business enablement — and you want to help define what the analytics engineer role becomes in an AI-native world — this is the role. What you'll do Build and maintain context infrastructure: Translate institutional business knowledge into structured formats — business glossaries, DBT model enrichment, semantic layer definitions in Omni Analytics — so that AI tools can answer questions accurately, not just confidently. Design and run evaluation frameworks: Develop predefined test cases, accuracy benchmarks, and validation workflows that measure whether AI-generated insights are trustworthy. Own the feedback loop between eval results and context improvements. Build and orchestrate AI agent systems: Help design, build, and iterate on the agent architectures that power our analytics tools — including prompt pipelines, tool orchestration, query routing logic, and guardrails that determine when AI should answer autonomously vs. escalate for human validation. Experiment and evaluate: Test prompt configurations, agent behaviors, and model outputs across different use cases — using eval results and accuracy metrics to drive continuous improvement. Develop internal AI tooling and workflows: Build tools and automations that improve DSA's own efficiency — identifying opportunities where AI can accelerate the team's work and executing on them. Build automated insight generation systems: Design and develop AI-powered systems that proactively surface patterns, anomalies, and meaningful changes in business data — delivering the right insights to the right people without waiting to be asked. Think less "answer questions" and more "anticipate them." 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. Surface insights from usage patterns: Monitor query logs and user behavior to identify gaps in context coverage, recurring questions that should become standard reporting, and opportunities to expand self-service capabilities. Who you are Technically curious and AI-forward: You're energized by LLMs, prompt engineering, and the evolving landscape of AI tooling. You've experimented with tools like Claude, ChatGPT, or Cursor — and you're eager to build systems around them, not just use them. A builder at heart: You have a bias toward making things. Whether it's a prototype, a pipeline, or a quick script to test an idea — you default to building rather than theorizing. You may not have deep software engineering experience, but you're comfortable picking up new technical skills and exploring unfamiliar domains, especially with AI tooling a ... (truncated, view full listing at source)