Senior Data Engineer, dbt Labs

• Expert-level SQL and Python skills
• 5+ years of experience as a data engineer, and 8+ years of total experience in software engineering (including data engineering roles)
• Strong knowledge of data lakehouse architecture, including storage layer design, table formats, and compute/query engine integration
• Experience defining and enforcing data contracts or schema standards in collaboration with upstream engineering teams
• Hands-on experience with modern orchestration tools like Airflow, Dagster, or Prefect
• Working knowledge of cloud infrastructure tooling, including Terraform, Helm, and Kubernetes
• Hands-on experience running Apache Spark in production, including job tuning, cluster sizing, and managing failures at scale
• A bias for action—able to stay focused and prioritize effectively in an ambiguous environment

Desirable

• As a Senior Data Engineer at dbt Labs, you'll build and maintain the core data platform infrastructure that powers our internal analytics and data products.
• You'll own the platform that makes data trustworthy at every layer — from the contracts that govern how it lands, to the infrastructure that stores, transforms, and delivers it across the business.
• This role is a part of a tight-knit, strategic team that combines strong technical execution with a bias for impact and cross-functional influence
• Own the architecture and operations of our data lakehouse, including object storage, table formats, maintenance, and query engine integrations
• Build and maintain the infrastructure layer that transforms and serves data reliably at scale—from raw landing zones through to curated, queryable datasets
• Partner with product engineering to establish data contracts and schema standards around event telemetry, ensuring data arrives in the lakehouse in a form that's reliable and ready for downstream use
• Drive decisions on data platform architecture, tooling, and engineering best practices across storage, compute, and access layers
• Enhance observability and monitoring of data infrastructure, including pipeline reliability, data freshness, and system performance
• Partner cross-functionally with teams across Analytics, Infrastructure, and Product to understand data needs and deliver impactful platform solutions
• 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 member of the software engineer team
• White boarding session with a cross functional stakeholder
• Final wrap up/ values discussion with Data Leader