Senior Software Engineer, Multi-Cloud

About Us

Since 2016, dbt Labs has been on a mission to help analysts create and disseminate organizational knowledge. dbt Labs pioneered the practice of analytics engineering, built the primary tool in the analytics engineering toolbox, and has been fortunate enough to see a fantastic community coalesce to help push the boundaries of the analytics engineering workflow. Today there are 30,000 companies using dbt every week, 100,000 dbt Community members, and over 4,100 dbt Cloud customers. You can learn more about our values here.  

About the Multi-Cloud Team: 

We’re looking for an experienced Software Engineer to help us build out a new multi-tenant offering in Azure. Over the past year, we have built a next-generation multi-cell architecture, and we are now ready to create a highly-available, highly scalable offering in Azure that is at parity with our AWS offering. We have long hosted enterprise customers in Azure single-tenant deployments and have a clear view of what success looks like as we expand to multi-tenancy.

You’ll be joining a collaborative team of engineers working at the intersection of application code and cloud infrastructure to create a reliable and performant platform. As a Senior Engineer on the team you will prioritize and tackle problems as they arise, working iteratively with customers and stakeholder to bring the new offerings to market. Today we serve some of the largest data-driven organizations in the world, enabling them to make decisions based on the knowledge at the core of their business. Your work directly impacts dbt Lab’s mission by expanding the organizations we serve and the data practitioners we reach.

In this role, you can expect to:

• Building a highly reliable, highly performant application on Azure through the lens of creating high-value multi-cloud abstractions
• Leverage a variety of languages and technologies: Golang, Python, Kubernetes, Terraform, Helm
• Drive scaling and automation initiatives across our cloud platform, contributing to quality and developer productivity
• Work across our entire product slate to ensure quality and performance, sometimes diving deep to evolve how features work
• Partner with teams throughout EPD

Qualifications:

• Have 5+ years experience in software engineering, including production experience supporting SaaS applications deployed on Azure
• Minimum requirement of Bachelors degree in related field (computer science, computer engineering, etc.) OR
• Completed enrollment in engineering related bootcamp

You are a good fit if you: 

• Have designed and built distributed cloud applications including containerized workloads in production on Azure
• Have a deep sense of empathy for developer experience and a drive to build solutions that enable our product teams to own and operate their features in multiple clouds
• Are eager to collaborate across team and group boundaries to aid the delivery of new offerings - both your team’s new deployments and the new features being built by our product teams
• Have the ability to break down complex technical concepts and communicate them to business stakeholders
• Have a systematic problem-solving approach coupled with strong communication skills and a sense of ownership and drive.

You'll have an edge if you: 

• You have experience working in multiple Cloud environments (AWS, Azure, GCP)
• Have excellent written communication skills

Compensation and Benefits:

• Salary: $154,000-$181,000
• Equity Stake
• Benefits - dbt Labs offers:
  • Unlimited vacation (and yes we use it!)
  • 401k w/3% guaranteed contribution
  • Excellent healthcare
  • Paid Parental Leave
  • Wellness stipend
  • Home office stipend, and more!

What to expect in the hiring process (all video interviews unless accommodations are needed):

• Interview with a Talent Acquisition Partner 
• Technical Interview with Hiring Manager
• Team Interviews 
• Final interview with leadership team member

#LI-RC1

Who we are

At dbt Labs, we have developed strong opinions on how companies should practice analytics.

Specifically, we believe that:

• Code, not graphical user interfaces, is the best abstraction to express complex analytic logic
• Data analysts should adopt similar practices and tools to software developers
• Critical analytics infrastructure should be controlled by its users as open source software
• Analytic code itself — not just a