Data Engineer, dbt Labs

• If you're excited by the challenge of building from the ground up, solving complex technical problems while utilizing cutting edge technology, and driving business strategy through data, this is your chance to make a lasting mark
• Are an expert in SQL and Python
• 2+ years of experience as a data engineer, and 4+ years of total experience in software engineering (including data engineering roles)
• Proficiency in at least one additional core Data Engineering language such as Scala, Java or Rust
• Hands-on experience with modern orchestration tools like Airflow, Dagster, or Prefect
• A bias for action—able to stay focused and prioritize effectively

Desirable

• As a Data Engineer at dbt Labs, you'll assist in designing, building, and owning core components of our data ecosystem—from infrastructure to pipelines to data products
• This data foundation is essential for enabling analytics, accelerating growth, and improving operational efficiency across the business
• You’ll be part of a tight-knit, strategic team that combines strong technical execution with a bias for impact and cross-functional influence
• This is a unique opportunity to join a team involved in using dbt Labs products daily to build the company's internal data capabilities
• This group is responsible for building and maintaining foundational data infrastructure that powers critical business decisions across the company
• With executive visibility and deep cross-functional impact, the work you do here will directly influence the trajectory of our growth
• Design, build, and maintain reliable, scalable data pipelines using dbt to support analytics and operational use cases
• Implement and enforce standards that ensure high data quality, consistency, and reliability across our internal data ecosystem
• Optimize query performance, manage compute costs, and ensure low-latency data access for downstream users
• Continuously evaluate and enhance existing systems, proactively identifying and resolving technical debt and bottlenecks
• Collaborate with analysts, business stakeholders, and engineers to understand data requirements and deliver relevant and sustainable solutions
• Build automation into data workflows to reduce manual overhead and improve data freshness, lineage tracking, and monitoring
• Provide product feedback by “dogfooding” new data infrastructure and AI technology

• Interview with a Talent Acquisition Partner
• Interview with Hiring Manager
• White boarding session with a member of the data team
• White boarding session with a cross functional stakeholder
• Final wrap up/ values discussion with Data Leader