Why This Job is Featured on The SaaS Jobs
This Software Engineer, Data role sits at a point many SaaS companies are actively investing in: turning product usage and go to market signals into reliable, decision-ready datasets. Airtable’s positioning as a no code platform moving toward AI native workflows makes instrumentation and measurement a first-order product concern, not a back-office function. That places data engineering close to how the platform evaluates adoption, agent impact, and overall performance at scale.
For a SaaS career, the work maps to durable problems: building governed pipelines, defining shared business tables, and creating a pattern language that keeps analytics consistent as teams and product surface area expand. The cross-functional interface with product, growth, and revenue stakeholders reflects how modern SaaS organizations operate, where the data layer is a dependency for prioritization, experimentation, and forecasting. The emphasis on operational reliability and warehouse performance also translates across SaaS environments with mature reporting needs.
This role tends to suit engineers who prefer ownership of foundational systems and who enjoy translating ambiguous questions into concrete data models. It aligns with professionals who like partnering beyond engineering while staying hands-on in SQL and programming. Comfort with iterative tooling, including emerging AI-assisted workflows, is a practical fit signal in this kind of SaaS stack.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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, 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 a Software Engineer, Data at Airtable, you'll make an enormous contribution to our data engineering efforts. You'll design and own mission-critical data pipelines to enable decision-making, partner with company leaders to create scalable data solutions, and launch innovative alerting and visualization solutions.
The Teams
We're hiring across two pods. Both teams work closely with Data Science and Analytics and are central to how Airtable understands itself as a business. You'll find your fit based on where your interests and experience align. And uniquely: 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, you're among the first to instrument it, understand its adoption, and help shape what comes next.
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.
GTM & Business Data Engineering This team owns the datasets that power go-to-market and business operations at Airtable. We build and govern the business critical pipelines and core tables used to report AI usage metrics across the company, create and maintain data models that support RevOps, Marketing, and BizTech stakeholders, and increasingly leverage AI tools, including writing Claude skills and incorporating AI context guidance, to accelerate and enhance our day-to-day pipeline work. If you're energized by working at the intersection of clean data architecture and cross-functional business impact, this is your team.
What you'll do
- 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.
- Design and maintain our foundational business tables in order to simplify analysis and reporting across the entire company, including AI usage metrics surfaced to executive stakeholders.
- Use AI tools as a daily part of how you work, from LLM-assisted pipeline development and debugging to exploring our catalog through AI-powered discovery, and bring a curiosity for where this tooling is heading next.
- Build and enforce a pattern language across our data stack, ensuring pipelines and tables are consistent, accurate, and well-understood.
- Continue to improve the performance and reliability of our data warehouse.
- Partner with data scientists, analytics engineers, and business stakeholders to translate ambiguous business questions into well-scoped data solutions.
Who you are
- You have 3-8+ years of professional experience designing, creating, and maintaining scalable data pipelines, preferably in Airflow.
- You've wrangled enough data to understand how often the complex systems that produce it can go wrong, and you build with that in mind.
- You are proficient in at least one programming language (preferably Python) and are willing to pick up others as the work demands.
- You are highly effective with SQL and understand how to write and tune complex queries.
- You're genuinely curious about how AI is reshaping data engineering and you're actively experimenting, not just watching from the sidelines. Whether that's using LLMs to write and debug pipelines faster, thinking through how to model agent behavior as data, or exploring what smarter data discovery could look like, you bring enthusiasm for figuring it out.
- You're passionate and thoughtful about building systems that enhance human understanding.
- You communicate with clarity and precision in written form and have experience conveying findings through graphs and visualizations.
