dbt Labs's Interview Process (2026)

Blog / dbt Labs's Interview Process (2026)
dbt Labs Interview Process
The dbt Labs software engineer interview process typically runs 3 to 4 weeks end-to-end and blends traditional software engineering with a strong emphasis on data engineering fundamentals. Most candidates report a consistent structure, though the exact format can vary by team and role.
  • Recruiter Screen: A 30-minute call covering your background, your interest in dbt Labs, and your experience working in remote or async environments.
  • Technical Phone Screen: A 60-minute live coding session, typically conducted on CoderPad, that usually includes both a SQL problem and a DSA question to assess your foundational technical range.
  • Virtual Onsite: Coding Round 1 (DSA): A live coding round focused on algorithmic problem-solving, with an emphasis on efficiency, edge case handling, and clean code.
  • Virtual Onsite: Coding Round 2 (Practical): A more applied coding round where you might build a small utility or work through a problem involving graph traversal or dependency management, reflecting dbt's core DAG architecture.
  • Virtual Onsite: System Design: A virtual whiteboard session where you design a scalable system, often themed around dbt's core product, such as an orchestration engine or a SQL compilation layer.
  • Virtual Onsite: Past-Project Deep Dive: A dedicated 45 to 60 minute discussion where you walk through a complex project you led or contributed to significantly, focusing on your technical decisions and the tradeoffs you made.
  • Virtual Onsite: Behavioral Round: A conversation around collaboration, values alignment, and developer empathy, which is how dbt Labs describes the ability to build tools that other technical practitioners genuinely enjoy using.
To prepare effectively, focus your study plan on these key areas that consistently come up throughout the process:
  • SQL: Complex SQL is tested throughout the process and is non-negotiable for any SWE role at dbt Labs.
  • Data Structures & Algorithms: Graph-based problems are especially common given dbt's DAG-driven architecture.
  • System Design: System design questions are themed around dbt's core product, covering orchestration, scalability, and SQL compilation.
  • Past-Project Deep Dive: A full dedicated round where you articulate the why behind your most complex technical work.
  • Behavioral: Focused on collaboration, developer empathy, and alignment with dbt's remote-first values.
1. SQLSQL is treated as a first-class engineering skill at dbt Labs, not an afterthought. Candidates report that struggling with window functions or CTEs is often a hard fail, even if your Python or Go skills are strong.Expect questions on complex window functions, recursive CTEs, and query optimization tradeoffs. Problems like User Session Analysis are a good example of the applied SQL thinking they look for.Spend focused time with SQL theory to sharpen your understanding of how queries execute, not just how to write them. Understanding performance tradeoffs will help you stand out.
2. Data Structures & AlgorithmsGraph problems are the most frequently reported DSA topic at dbt Labs, which makes sense given that dbt's entire model dependency system is built on a Directed Acyclic Graph. Practicing problems like Dependency Graph Cycle Detection, Course Schedule, and Course Schedule II will put you in a strong position.Beyond graphs, the practical coding round may involve building a small utility or working through dependency management logic. Problems like Service Dependency Graph and Employee Management Chain reflect the kind of real-world framing you might encounter.For broader preparation, work through our graphs questions and trees questions to cover the structural patterns that come up most. Our top 100 DSA questions are also a solid way to fill any gaps in your fundamentals.
3. System DesignSystem design at dbt Labs tends to be product-grounded. Recent prompts include designing a dbt Cloud orchestration system that handles scheduling, retries, and dependency-aware execution, or designing a metrics layer that compiles to SQL across multiple data warehouses.To prepare, make sure you are comfortable with system design core concepts and how distributed systems handle scheduling and failure recovery. Familiarity with orchestration tools like Airflow and data warehouses like Snowflake or BigQuery will help you speak the interviewer's language.Practice drawing out your architectures with our System Design AI Whiteboard and review concepts on our High-Level Design topic page to build a repeatable approach for structuring your answers.
4. Past-Project Deep DiveUnlike most companies that weave project questions into behavioral rounds, dbt Labs dedicates a full interview to one project. They want to understand not just what you built, but why you made the technical choices you did and what you would do differently.Pick a project where you had real ownership and can speak to meaningful tradeoffs. The more specific you are about the problem, the constraints, and the reasoning behind your decisions, the better this round tends to go. Look into our portfolio projects for inspiration and guidance on what to build and how to structure a project from start to finish.dbt Labs describes itself as a writing-focused engineering culture, so practice articulating your technical decisions clearly and concisely, as if you were writing an internal design document for a skeptical peer.
5. BehavioralThe behavioral round at dbt Labs centers on collaboration and what they call developer empathy, specifically how you think about building tools that other engineers actually want to use. Expect questions about how you would make a feature easier to debug or document for an analytics engineer.Come prepared with examples that show cross-functional collaboration, clear communication under ambiguity, and a user-focused mindset. Since dbt Labs is fully remote, they also look for evidence that you communicate well in writing and can work effectively without being in the same room as your team.Structure your answers using the STAR principle to keep your responses focused and easy to follow. Our Behavioral Interview Course and Behavioral Playbook are good resources to work through before the interview.
Conclusiondbt Labs has a focused and well-structured process, but the SQL and graph fundamentals are non-negotiable, so start there. For a step-by-step plan covering every stage of the process, follow our dbt Labs Interview Roadmap and work through it systematically.

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