Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500000 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 were 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.

Youll be one of the first hires shaping this discipline. That means you wont just use AI tools youll build the systems that make them accurate design the workflows that make them trustworthy and partner across the business to drive adoption. If youre 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: Were particularly interested in candidates with 14 years of professional experience.

What youll 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&As own efficiency identifying opportunities where AI can accelerate the teams 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: Youre energized by LLMs prompt engineering and the evolving landscape of AI tooling. Youve experimented with tools like Claude ChatGPT or Cursor and youre eager to build systems around them not just use them.
• A builder at heart: You have a bias toward making things. Whether its 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 youre comfortable picking up new technical skills and exploring unfamiliar domains especially with AI tooling accelerating whats possible.
• Analytically grounded: Youre 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: Youre more interested in whats emerging than whats established. You evaluate tools based on what they enable not how long theyve been around and youre quick to adopt new approaches when theyre 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: Youre 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 dont exist yet. Youre 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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