Airtable
Software Engineer, Data
About this role
Airtable seeks a Software Engineer, Data to design and maintain mission-critical data pipelines that power business intelligence and product analytics across the platform. You'll work closely with cross-functional teams to build scalable data solutions, instrument AI-native features, and enable data-driven decision-making at scale.
What you'll do
- Design, build, and maintain foundational data pipelines and business tables using tools like Airflow
- Partner with data science, product, and business teams to translate requirements into well-scoped data solutions
- Instrument and measure AI agent adoption and usage metrics for executive stakeholders
- Develop and enforce consistent patterns across the data stack to ensure reliability and clarity
- Optimize data warehouse performance and reliability
- Create alerting and visualization solutions to surface key insights
What they're looking for
- Data pipeline design and maintenance (3-8+ years experience)
- Python or other programming languages
- Advanced SQL and query optimization
- Airflow or similar orchestration tools
- Data modeling and warehouse design
- AI/LLM tools for engineering workflows
- Clear communication and data visualization
- Systems thinking and debugging complex data issues
Benefits
- Competitive compensation based on location and experience
- Restricted stock units
- Incentive compensation opportunities
- Comprehensive benefits package
- Work with Fortune 100 customers
- Multiple office locations (San Francisco, Austin, New York)
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Airtable
Airtable builds a no-code platform used by hundreds of thousands of organizations and Fortune 100 companies to create and manage custom applications. The company is hiring full-stack engineers, backend engineers, infrastructure engineers, and data engineers to develop AI-powered features, scale foundational systems, and build data pipelines that power the platform.
- Website
- airtable.com
Likely interview questions
- Walk us through a complex data pipeline you've designed in Airflow. How did you handle failure scenarios and data quality issues?
- Describe your experience building and maintaining foundational data models or business tables. How do you ensure consistency and accuracy across stakeholders?