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dbt Labs is seeking a Staff Software Engineer to join its Shared Services group. The successful candidate will be the technical backbone for the team, providing technical guidance, leadership, and direct technical contributions. This is an opportunity to build customer-facing APIs and internal tools used by a fast-growing customer base and engineering organization.

Responsibilities:

  • Define the architecture and guide the implementation of dbt Cloud’s Shared Services and public APIs.
  • Manage trade-offs, priorities, and deliverables.
  • Work with teammates to build confidently and quickly via high-leverage tooling, mentorship, and knowledge of industry trends and practices.
  • Help define quality standards and drive the overall reliability of our services.
  • Participate in daily stand-ups, mentee sessions, and pair programming.

Requirements:

  • 8+ years of experience as a software engineer.
  • Minimum requirement of bachelor's degree in a related field (computer science, computer engineering, etc.) OR completed enrollment in engineering related bootcamp.
  • Experience with Python, Go, or similar languages to create scalable full-stack applications.
  • Experience working on large-scale, event-driven systems.
  • Experience with Enterprise SaaS Products.
  • Strong backend skills.

dbt Labs offers:

  • Unlimited vacation
  • Excellent healthcare
  • Group Retirement Savings Program
  • Paid Parental Leave
  • Wellness stipend
  • Home office stipend
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dbt Labs

dbt Labs is a pioneering company at the forefront of the data transformation movement. It empowers data teams to build and manage reliable data pipelines using the dbt (data build tool) framework. Focusing on analytics engineering, dbt Labs offers a collaborative and version-controlled environment for data modeling and transformation. The company fosters a vibrant community, driving innovation and best practices in data workflows. dbt Labs enables organizations to unlock the full potential of their data by promoting efficiency, accuracy, and collaboration in the data transformation process, and making data more accessible.