Manager, Data Engineering

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.

At Airtable, we're passionate about democratizing software creation, empowering anyone to build powerful and flexible tools without writing code. With our shift to an AI-native platform, customers can now generate full apps and deploy AI agents directly into their workflows. Data engineering plays a critical role in this evolution by delivering the insights our teams rely on to improve user experience, measure agent impact, and understand how the business is performing at scale.

As the Data Engineering Manager at Airtable, you'll lead the GTM & Business Data Engineering team, the team that owns the datasets powering go-to-market and business operations across the company. Your team builds the business-critical pipelines and core tables that report AI usage metrics company-wide, maintains the data models that support RevOps, Marketing, and Finance stakeholders, and increasingly uses AI tools (Claude skills, AI context guidance, and emerging tooling) as a core part of how the team works every day. You'll set the technical bar, shape the team's AI craft, own team operations, and partner with company leaders on data strategy.

One thing that makes this role unique: the platform you're measuring and building on is the same one your customers use every day. When Airtable ships a new AI agent capability, your team is among the first to instrument it, understand its adoption, and help shape what comes next.

Please note: while we employ a hybrid working model at Airtable (flexible in working from the office or elsewhere), we are looking to hire candidates at this level that are based in San Francisco or New York City who are open to coming into the office at least ~2-3 times/week for team collaboration.

What you'll do

• Lead a team of data engineers. Coach and develop your reports, maintain team health, and stay close enough to the work to set the technical bar.
• Own team standards and operations. Set and enforce the pattern language that keeps pipelines, tables, and naming consistent at scale. Lead on call, incident response, monitoring, and the code review standards that keep the team shipping.
• Drive reliability as a system property. Anchor delivery around measurable reliability goals including SLAs for landing time and accuracy.
• Make data a product. Sharpen our data models for AI billings and usage so executive stakeholders can clearly see how our AI features are landing in the business. Treat the team's outputs as products with quality, observability, and trust built in from the start.
• Shape how the team uses AI. Set the bar for how Claude Code, Hyperagent, and emerging tooling are used on the team. Establish the patter