Since 2016, dbt Labs has been on a mission to help analysts create and disseminate organizational knowledge. We've pioneered the practice of analytics engineering, built the primary tool in the analytics engineering toolbox, and have been fortunate enough to see a fantastic community coalesce to help push the boundaries of the analytics engineering workflow. Today, there are 50,000 teams using dbt every week, 100,000 dbt Community members, and over 5,100 dbt Cloud customers.
As of February 2025, we surpassed $100 million in annual recurring revenue (ARR), scaling from $2M ARR to this milestone in only four years. Simultaneously, we've exceeded the 5,000 customer mark, with 85% year-over-year growth in adoption among Fortune 500 companies. This momentum is fueled by dbt’s critical role as the data control plane for enterprise data teams around the world, who rely on the technology to transform data into reliable, actionable business insights. We're rapidly growing and looking for passionate people to join our global team. Learn more about what makes us special by checking out our values here.
About the role:
Are you excited about the challenge of building AI/ML infrastructure from the ground up in a greenfield environment? Our team is tackling one of the biggest challenges in data warehouse cost visibility and optimization—helping executive stakeholders understand and reduce their spend within their dbt projects. Unlike external vendors, we’re bringing cost visibility directly into dbt, enabling users to detect anomalies, predict spend, and automate remediation. This is an opportunity to own the AI/ML foundation for a highly anticipated product that will transform how organizations manage their data costs.
In this role, you can expect to:
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Design and build the AI/ML infrastructure to power cost anomaly detection and predictive analytics.
- Lay the foundation for ML-driven cost optimizations—detecting issues and automating remediation within dbt.
- Work hands-on with Snowflake, Kafka, PyTorch, TensorFlow, Ibis, and Pandas to process billions of data points.
- Collaborate cross-functionally with Product, Engineering, and Design to integrate ML models into dbt workflows.
- Provide technical leadership in defining the AI/ML strategy for cost optimization and efficiency.
- Influence the growth of our AI/ML engineering team as we scale.
You are a good fit if you have:
- Extensive hands-on experience in ML infrastructure, including building pipelines and frameworks from scratch.
- Expertise in anomaly detection, fraud detection, predictive analytics, and confidence scoring.
- Strong Python skills, with proficiency in PyTorch, TensorFlow, and Spark (PySpark preferred, not Java-based Spark).
- Experience working with data warehouses (Snowflake), streaming systems (Kafka), and large-scale ML systems.
- A track record of delivering production-ready ML systems—not just research or Jupyter notebooks.
- Ability to thrive in a fast-paced, fully remote, distributed team environment.
You'll have an edge if you have:
- Experience in ML model evaluation, benchmarking, and automated remediation systems.
- A background in building scalable ML platforms that process billions of data points.
- Familiarity with cost optimization techniques (CostOps/FinOps) within data platforms and cloud environments.
- Prior experience in ML infrastructure engineering at a high-growth startup or large-scale platform.
Qualifications:
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5+ years of experience in ML infrastructure, AI/ML engineering, or data engineering.
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Bachelor’s degree in a related field, or equivalent professional experience. OR
- Completed enrollment in related bootcamp
- Experience with high-scale ML infrastructure in a fully remote, asynchronous environment.
This is a high-impact, hands-on role where you’ll play a pivotal part in shaping the AI/ML foundation at dbt Labs. If you're excited about solving complex data challenges and building something from scratch, we'd love to hear from you!
- We offer competitive compensation packages commensurate with experience, including salary, equity, and where applicable, performance-based pay. Our Talent Acquisition Team can answer questions around dbt Labs' total rewards during your interview process.
- The typical starting salary range for this role is: $172,000 - $207,900 USD
- The typical starting salary range for this role in the select locations listed is: $191,000 - $231,000 USD
- Equity Stake
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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
- Systems Design & Product Partner
- Final interview with leadership team member
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