Partner with Product and ML teams to measure the impact of new features, workflows, and personalization systems Build and maintain robust dbt models, dashboards, and self-serve analytics tools using Snowflake, dbt, and BI platforms

Deliver trusted, reusable data products: foundational datasets that power analytics, reporting, in-app features, and AI, anchored on a joinable customer/account spine across product, billing, and CS context. Stand up data observability: quality checks, freshness, lineage, schema drift, and incident response, so the business can trust what it sees.

Product & AI Data Infrastructure This team builds and owns the foundational data pipelines that power product analytics across Airtable. As Airtable shifts to an AI-native platform, our work increasingly involves instrumenting and measuring AI product usage, building event pipelines for AI agents, surfacing AI-native adoption metrics in core business tables, and developing AI-powered data discovery tooling, including vector search over our catalog metadata. We partner closely with product analytics, product engineering, and data infrastructure to turn business questions into well-modeled, trustworthy data. Work across our engineering organization and stakeholders from data science, growth, sales, marketing, and product to understand the data needs of the business and produce pipelines, data marts, and other solutions that enable better decision-making.