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.

At Airtable were 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 Senior Data Engineering Manager at Airtable youll 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. Youll set the technical bar shape the teams AI craft own team operations and partner with company leaders on data strategy.

One thing that makes this role unique: the platform youre 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 youll 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 teams 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.

Who you are

• 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. Youre not the one who always has the answer. Youre 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. Youre proficient in Python and highly effective with SQL including tuning complex queries.
• Someone with hard-won instincts for stability. Youve 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 youve operationalized those instincts on teams youve 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 dont just use AI tools. You treat AI as a high-trust collaborator core to how you solve problems and youve established the patterns that raised the bar for a whole team. You can point to concrete AI tooling youve shipped on a data team: Claude skills LLM-assisted pipeline work automated PR fixes AI-powered discovery. We want to see what youve built.
• A clear communicator. You write with clarity and precision can translate technical context for executives and tell stories with data.

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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