DBT Interview Questions

Introduction

DBT (Data Build Tool) has become the standard for modern data transformation. It empowers data teams to transform, test, document, and deploy SQL-based data models directly within cloud data warehouses like Snowflake, BigQuery, Redshift, and Databricks.

Whether you’re applying for a role as a Data Analyst, Analytics Engineer, or Data Engineer, these questions will help you crack any DBT-related interview.

Basic DBT Interview Questions

1. What is DBT?

Answer: DBT (Data Build Tool) is an open-source command-line tool that enables analysts and engineers to transform raw data into trusted datasets using SQL.

2. What does DBT stand for?

Answer: DBT stands for Data Build Tool.

3. Where does DBT fit in the ELT pipeline?

Answer: DBT performs the Transform step in ELT—after data is extracted and loaded into the warehouse.

4. What are the benefits of using DBT?

• Modular SQL development

• Version control with Git

• Built-in testing

• Auto documentation

• Incremental loads

• CI/CD deployment support

5. What are DBT Core and DBT Cloud?

• DBT Core is CLI-based and open source.

• DBT Cloud is a managed service with web IDE, job scheduler, and CI/CD tools.

6. What data warehouses does DBT support?

• Snowflake

• BigQuery

• Redshift

• Databricks

• Postgres

7. What is a DBT model?

Answer: A model is a SQL file that defines a transformation. It’s stored in the models/ directory and materialized as a table or view in the data warehouse.

DBT Project & Models

8. How do you create a DBT project?

9. What are materializations in DBT?

10. Where is materialization configured?

• In dbt_project.yml or using the {{ config(materialized='table') }} Jinja tag in the SQL model.

11. What is the use of ref()?

Answer: {{ ref('model_name') }} creates a dependency and allows DBT to manage build order and lineage.

12. How does DBT handle dependencies between models?

Through the use of ref() which automatically generates a DAG (Directed Acyclic Graph).

13. What is an ephemeral model?

A temporary model used as a CTE. It’s not materialized in the database.

DBT Testing and Validation

14. What are the types of DBT tests?

• Schema tests (e.g., not_null, unique)

• Custom tests (SQL-based logic)

15. Give an example of a schema test.

16. What is a custom test in DBT?

A SQL file that returns rows if the test fails.

17. How do you run tests in DBT?

18. What happens if a test fails?

The test result shows failed status. It doesn’t stop model builds unless configured in CI/CD pipelines.

19. What are macros in DBT?

Reusable Jinja functions that help in logic abstraction.

Incremental Models and Snapshots

20. What is an incremental model in DBT?

It only processes and loads new or updated records, improving performance.

21. How do you define an incremental model?

22. What is a DBT snapshot?

It captures historical records and tracks changes using slowly changing dimensions (SCDs).

23. What strategies are used in snapshots?

• timestamp strategy

• check strategy

24. Where do you store snapshot configurations?

In the snapshots/ directory with snapshot blocks and metadata.

Also Read: DBT Interview

DBT Documentation & Sources

25. How do you document a DBT model?

Use schema.yml to define descriptions for models and columns.

26. How do you generate docs?

27. What is the purpose of source()?

{{ source('raw', 'orders') }} allows referencing external raw tables safely and tracks freshness.

28. What is freshness in DBT sources?

It checks the last updated timestamp to detect stale data.

29. What are seeds in DBT?

CSV files in the seeds/ folder that can be loaded into the warehouse.

30. How do you load seed data?

Advanced DBT Concepts

31. What is the use of Jinja in DBT?

Jinja allows for dynamic SQL generation, conditions, loops, and variables.

32. What is this in DBT?

{{ this }} refers to the model being compiled or materialized (i.e., its fully qualified name in the warehouse).

33. How do you use variables in DBT?

Pass using CLI:

Use in SQL:

34. What are packages in DBT?

Reusable code modules shared in packages.yml. Example: dbt_utils.

35. How do you install a package?

Then run:

36. What is the use of dbt_utils?

Provides helper macros for common SQL patterns like get_column_values, pivot, generate_surrogate_key.

Deployment & CI/CD

37. How do you schedule jobs in DBT Cloud?

Using the Jobs tab, configure job runs and assign schedules (cron or visual scheduler).

38. How do you implement CI/CD with DBT Cloud?

• Connect with GitHub/GitLab

• Auto-run dbt run or dbt test on PR

• Enforce validations before merge

39. What is an environment in DBT Cloud?

A logical grouping of settings like credentials, targets, and branches (e.g., dev, staging, prod).

40. How do you implement promotion from dev to prod?

• Create branches for each environment

• Merge to main after passing tests

• Run jobs in prod environment

Scenario-Based DBT Questions

41. How would you optimize performance in a large DBT project?

• Use incremental models

• Avoid SELECT *

• Break logic into layers

• Use ephemeral models for intermediate logic

• Partition tables when possible

42. How would you debug a failed model build?

• Run with --debug

• Check warehouse logs

• Validate SQL separately

• Review config or model dependencies

43. Your incremental model is duplicating rows—how do you fix it?

Ensure:

• is_incremental() condition is correct

• Deduplicate source

• Use surrogate keys

44. How do you handle late-arriving dimensions?

• Use snapshots

• Apply updated_at filters

• Use merge in incremental models (warehouse-specific)

45. How do you manage environment-specific variables (e.g., schema names)?

Use target.name and conditional logic in configs:

Security & Governance

46. How does DBT ensure data integrity?

• Testing (not_null, unique, relationships)

• Version control (Git)

• Snapshots for audit trails

47. How do you handle sensitive data in DBT?

• Use warehouse-level masking

• Restrict access via grants

• Avoid exposing sensitive columns in downstream models

48. Can DBT enforce role-based access control (RBAC)?

Not directly, but it supports integration with tools like Snowflake RBAC via grants statements in models or post-hooks.

Role-Specific DBT Questions

49. As a Data Analyst, how would you use DBT?

• Query models in BI tools

• Build light transformations

• Contribute to schema documentation

• Add tests for data quality

50. As an Analytics Engineer, what’s your role in DBT?

• Own transformation logic

• Structure the project

• Create reusable macros

• Build tests and deploy code

51. What’s the difference between staging and mart models?

• Staging: Source-cleaned models

• Marts: Final business models for reporting (fact/dim)

Real-Time Use Cases

52. Describe your DBT project structure.

53. How do you build a SCD Type 2 in DBT?

Use snapshots to track changes over time with check or timestamp strategy.

54. How do you audit a DBT model?

• Use audit logs from DBT Cloud

• Add logging macros in SQL

• Track run metadata with dbt artifacts

55. How do you handle frequent schema changes?

• Use source freshness checks

• Abstract logic using macros

• Use int_ models as buffers

Conclusion

These 60+ DBT interview questions cover a wide range of topics—from project setup and testing to deployment and optimization. Mastering them will help you confidently tackle analytics engineering and data engineering roles in modern, cloud-based data teams.