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.

Note on Leveling: We’re particularly interested in candidates with 1–4 years of professional experience.

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 DS&A'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 accelerating what's possible.
• Analytically grounded: You're SQL-proficient and have experience with modern data tools (dbt, Databricks, Snowflake, or similar). You have strong intuition for when data "looks wrong" and can validate query logic and troubleshoot issues independently.
• 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 — and you're quick to adopt new approaches when they're better.
• A clear communicator and strong writer: Context engineering is fundamentally a writing discipline. You can translate complex business logic into precise, structured documentation that both humans and LLMs can interpret.
• Business-minded: You're genuinely curious about how the business works — how we sell, how customers use the product, what metrics matter and why. You ask "what decision does this support?" not just "is the SQL correct?"
• Energized by building something new: AI-powered analytics is an emerging discipline — the best practices don't exist yet. You're excited to learn as you go, experiment, iterate, and help shape the playbook rather than follow one.
• Independent and proactive: You can own workstreams end-to-end — from scoping the problem, to building the solution, to iterating based on feedback. You bring ideas to the table and move things forward without waiting for step-by-step direction.
• Experience: 2 - 4 years in data-related roles (analytics engineer, data analyst, data scientist, or similar), including experience partnering with business stakeholders. Experience in SaaS or tech environments preferred.
• Must-Have Skills
  • 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 — has applied them to build or accelerate real work
  • 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. We welcome people of different backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status or any characteristic protected by applicable federal and state laws, regulations and ordinances. Learn more about your EEO rights as an applicant. 

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