Senior Data Engineer

dbt Labs is the pioneer of analytics engineering, helping data teams transform raw data into reliable, actionable insights. As a Senior Data Engineer, you'll build and maintain the core data platform infrastructure that powers internal analytics and data products, ensuring data reliability and governance at scale.

Responsibilities:

• 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

Requirements:

• 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