Manager, Data Engineering

Airtable•Austin, TX

20h•$261,900 - $339,900•Hybrid

About The Position

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.

Requirements

An engineering manager who multiplies the team. You bring 2+ years managing a data, analytics, or platform engineering team with a track record of growing engineers and shipping outcomes, not just running standups. You're not the one who always has the answer. You're the one who makes sure the team finds it.
A strong data engineer first. You have 10+ years building scalable data pipelines (preferably Airflow) and can still pair on the hardest problems on the team. You're proficient in Python and highly effective with SQL, including tuning complex queries.
Someone with hard-won instincts for stability. You've wrangled enough data systems to know how often things go wrong. You think in SLAs, observability, and incident reduction rather than just feature velocity, and you've operationalized those instincts on teams you've actually led.
A data-as-product thinker. You treat data as a product and can act as its owner, designing for quality, trust, and the stakeholders who depend on it downstream.
An AI-native builder, not a spectator. You don't just use AI tools. You treat AI as a high-trust collaborator core to how you solve problems, and you've established the patterns that raised the bar for a whole team. You can point to concrete AI tooling you've shipped on a data team: Claude skills, LLM-assisted pipeline work, automated PR fixes, AI-powered discovery. We want to see what you've built.
A clear communicator. You write with clarity and precision, can translate technical context for executives, and tell stories with data.

Responsibilities

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 patterns that make AI a high-trust collaborator on data work, with a team-level goal of roughly 30% time savings on pipeline development, debugging, and on call toil.
Partner across the business and represent the team to executives. Work with leaders across data science, growth, sales, marketing, product, and finance to turn ambiguous business questions into well-scoped solutions. Translate engineering work into business outcomes and make the case for investment.