Staff Software Engineer, Quality and Release Platform
New
D
dbt LabsData Analytics Engineering
US - RemoteFull-TimeStaff
Salary207,000 - 279,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonKubernetesGoRustCI/CDTerraformGitHub ActionsHelm
Requirements
- 8+ years of software engineering experience, with significant time in platform, infrastructure, release engineering, or developer tooling.
- A track record of leading technical strategy and architecture for complex, production-scale CI/CD, code quality, or platform systems.
- Deep experience with one or more of the following: Helm, ArgoCD, Terraform, GitHub Actions, or Kubernetes.
- Strong background in Python, Go, or Rust for automation, platform tooling, or systems development.
- Passion for code quality and experience building or improving tools, linters, static analysis, testing frameworks, or CI checks.
- Demonstrated ability to drive cross-team initiatives and influence engineering-wide practices and standards.
- Excellent communication skills.
- Demonstrated interest or hands-on experience with AI-assisted development tools and practices.
- Experience working asynchronously as part of a fully remote, distributed team.
Responsibilities
- Define and drive the technical strategy and architecture for our CI/CD platform, release management systems, and code quality platform.
- Design and build tooling, frameworks, and automation that help engineering teams maintain and improve code quality across the organization.
- Lead high-impact initiatives that improve automation, observability, and self-service capabilities for engineers across the organization.
- Mentor and level up other engineers on the team, fostering a culture of technical excellence and continuous improvement.
- Collaborate across teams and with engineering leadership to identify systemic challenges in our delivery and quality processes and architect solutions to address them.
- Evolve our release architecture to support dbt Cloud's multi-cloud, cell-based infrastructure at scale.
- Establish best practices and standards for build pipelines, release workflows, code quality, and infrastructure-as-code that are adopted across engineering.
- Serve as a thought leader in engineering's internal AI strategy — evaluating AI-assisted development tools, defining adoption practices and guardrails, and enabling developers to use AI effectively across the org.
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