Senior Data Engineer

dbt Labs is the pioneer of analytics engineering, helping data teams transform raw data into reliable, actionable insights. Since 2016, we’ve grown from an open source project into the leading analytics engineering platform, now used by over 90,000 teams every week, driving data transformations and AI use cases. 

As of February 2025, we’ve surpassed $100 million in annual recurring revenue (ARR) and serve more than 5,400 dbt Platform customers, including AstraZeneca, Sky, Nasdaq, Volvo, JetBlue, and SafetyCulture. 

We’re backed by top-tier investors including Andreessen Horowitz, Sequoia Capital, and Altimeter. At our core, we believe in empowering data practitioners:

• Reliable, high-quality data is the fuel that propels AI-powered data engineering.

• AI is changing data work, fast. dbt’s data control plane keeps data engineers ahead of that curve.
• We empower engineers to deliver reliable, governed data faster, cheaper, and at scale.

dbt Labs is now synonymous with analytics engineering, defining the modern data stack and serving as the data control plane for enterprise teams around the world. And we’re just getting started.. We’re growing fast and building a team of passionate, curious people across the globe. Learn more about what makes us special by checking out our values.

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.

This is a unique opportunity to work on infrastructure that sits at the center of how dbt Labs runs as a business — with executive visibility, deep cross-functional reach, and the added dimension of dogfooding the very products we build. If you're excited by the challenge of solving hard platform problems with cutting-edge tooling and making a direct, lasting impact on company growth, this role is for you.

• 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

• 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

• Experience developing and scaling dbt projects
• Hands-on experience with Apache Iceberg or other open table formats in production, including multi-region or multi-cloud deployments
• Experience designing platform infrastructure that serves multiple downstream teams and use cases
• Experience working in a SaaS or high-growth tech environment

• Interview with a Talent Acquisition Partner
• Interview with Hiring Manager
• Team Interviews
• Final wrap up/ values discussion with Data Leader