Requirements & tools
Airtable
The Senior Manager, Data Engineering at Airtable leads the GTM and Business Data Engineering team. Core responsibilities include coaching and developing a team of data engineers, owning team operational standards (naming, pipeline patterns, on-call, incident response, code review), and driving measurable reliability outcomes through SLAs on landing time and accuracy. The role sharpens data models for AI billing and usage metrics so executive stakeholders can clearly see how AI features are landing in the business. A distinctive element of this position is shaping how the team uses AI tooling — establishing patterns for Claude Code, Hyperagent, and emerging tools as high-trust collaborators on data engineering work. Required: experience leading data engineering teams at scale, fluency in modern data stack tooling (dbt, Snowflake or equivalent, Airflow), and a strong perspective on how AI tooling reshapes data engineering practice. The compensation at $305,600 reflects San Francisco-tier pay banding. Hybrid 2-3 days per week in SF, NYC, or Austin office.
To apply for this job email your details to artem.leghnev@hotger.com
Role context
Senior Managers of Data Engineering at no-code platforms lead the teams building GTM and business data infrastructure that powers AI product decisions across the company. At Airtable, this role owns the data pipelines reporting AI usage metrics, the data models supporting RevOps, Marketing, and Finance, and the team practices around AI-native tooling including Claude Code and emerging skill frameworks. Airtable serves 80% of the Fortune 100 and is shifting toward an AI-native app platform; data engineering plays a central role in measuring how AI agents are adopted and refining product strategy. The position is hybrid with 2-3 days in San Francisco, New York City, or Austin offices.
Quick facts
Frequently Asked Questions
Does the Senior Manager Data Engineering role still write code or is it pure people management?
The role expects "stay close enough to the work to set the technical bar" per the job description — meaning hands-on code review, occasional production debugging, and active pattern-language setting, but not regular feature development. Practically, expect 70-80% management activity (coaching, planning, stakeholder partnership) and 20-30% deep technical involvement (architecture decisions, code review, AI tooling exploration).
How does Airtable approach AI tooling on engineering teams?
Airtable is shifting to AI-native both as a product (apps generated from natural language) and as an engineering practice. Data engineering specifically uses Claude Code skills, AI context guidance, and emerging tools as routine collaborators. This role is expected to set the patterns for how those tools integrate into the team's daily workflow — what gets automated, what stays human-owned, and how trust calibration develops over time.
What is the size and structure of the GTM and Business Data Engineering team at Airtable?
The team this role leads typically sits at six to twelve data engineers split across pipeline ownership areas (RevOps data, Marketing data, Finance data, AI usage metrics). The team partners with adjacent platform engineering, analytics engineering, and ML platform teams. Reporting relationship is to the VP of Data Engineering or Director of Engineering Platform, depending on current organizational structure.