Top 26 dbt Interview Questions and Answers for 2024

Intermediate dbt Interview Questions 

Now that we've covered basic dbt questions, here are some intermediate-level dbt questions. These focus on specific technical aspects and concepts.

What are sources in dbt? 

In dbt, sources are the raw data tables. We don't directly write SQL queries on those raw tables — we specify the schema and table name and define them as sources. This makes it easier to refer to data objects in tables.  

Imagine you have a raw data table in your database called orders in the sales schema. Instead of querying this table directly, you would define it as a source in dbt like this:

Define the source in your sources.yml file:

version: 2

sources:
  - name: sales
    tables:
      - name: orders

Use the source in your dbt models:

Once defined, you can refer to the raw orders table in your transformations like this:

SELECT *
FROM {{ source('sales', 'orders') }}

This approach abstracts the raw table definition, making it easier to manage and ensuring that if the underlying table structure changes, you can update it in one place (the source definition) rather than every query.

Benefits of using sources in dbt:

• Organization: Sources organize your raw data and make it easier to manage within a project.
• Abstraction: You abstract the schema details, reducing errors and enhancing flexibility.
• Documentation: Defining sources helps create better documentation for your raw data inputs.

What is a dbt model?

A dbt model is essentially a SQL or Python file that defines the transformation logic for raw data. In dbt, models are the core component where you write your transformations, whether aggregations, joins, or any kind of data manipulation.

• SQL models in dbt use SELECT statements to define transformations and are saved as .sql files.
• Python models, introduced with dbt's support for Python, are saved as .py files and allow you to use Python libraries like pandas to manipulate data.

SQL model example:

A SQL model transforms raw data using SQL queries. For example, to create a summary of orders from an orders table:

--orders_summary.sql
WITH orders_cte AS (
    SELECT
        customer_id,
        COUNT(order_id) AS total_orders,
        SUM(order_amount) AS total_revenue
    FROM {{ ref('orders') }}
    GROUP BY customer_id
)

SELECT *
FROM orders_cte

In this example:

• The orders_summary.sql model creates a summary of total orders and revenue for each customer using SQL.
• The model references the orders table (already defined as a dbt model or source).

Python model example:

A Python model manipulates raw data using Python code. It can be especially helpful for complex logic that might be cumbersome in SQL.

# orders_summary.py
import pandas as pd

def model(dbt, session):
    # Load the source data
    orders = dbt.ref("orders").to_pandas()

    # Perform transformations using pandas
    orders_summary = orders.groupby('customer_id').agg(
        total_orders=('order_id', 'count'),
        total_revenue=('order_amount', 'sum')
    ).reset_index()

    return orders_summary

In this example:

• The Python model uses pandas to transform the data by grouping the orders by customer and calculating the total number of orders and revenue.
• The result is then returned as a DataFrame, which dbt can integrate into its pipeline.

How would you create a dbt model? 

Here’s how to create a dbt model: 

• Create a directory under the models folder in the dbt project. 
• Add a new text file with a .sql extension within the directory (or .py if it’s a Python model).
• Now, write a SQL query or code to transform the raw data. 
• Execute the dbt run command to apply the transformation and create the model.

How does dbt handle dependencies between models?

dbt manages model dependencies using the ref() function, which creates a clear dependency chain between models. 

When you define a transformation in dbt, rather than directly referencing tables in your warehouse, you reference other dbt models using the ref() function. This ensures that dbt builds the models in the correct order by identifying which models depend on others.

For example, if you have a model orders_summary that depends on the orders model, you would define it like this:

WITH orders AS (
    SELECT * FROM {{ ref('orders') }}
)
SELECT
    customer_id,
    COUNT(order_id) AS total_orders,
    SUM(order_amount) AS total_revenue
FROM orders
GROUP BY customer_id

In this example, the {{ ref('orders') }} function ensures that the orders model is built before orders_summary, since orders_summary relies on the data in the orders model.

What are macros in dbt, and how can you run them? 

Macros in dbt are reusable blocks of SQL code written using the Jinja templating engine. They allow you to automate repetitive tasks, abstract complex logic, and reuse SQL code across multiple models, making your dbt project more efficient and maintainable. 

Macros can be defined in .sql files within the macros directory of your dbt project.

Macros are particularly useful when you need to perform similar transformations across multiple models or need environment-specific logic, such as using different schemas or modifying date formats based on deployment environments (e.g., development, staging, or production).

Example of creating macros:

• Date formatting macro: You can create a macro to standardize date formatting across models. Instead of repeating date format logic, you can create a macro like this:

-- macros/date_format.sql
{% macro format_date(column) %}
FORMAT_TIMESTAMP('%Y-%m-%d', {{ column }})
{% endmacro %}

Usage in a model:

SELECT
    customer_id,
    {{ format_date('order_date') }} AS formatted_order_date
FROM {{ ref('orders') }}

In this example, the format_date macro is used to standardize the order_date column's format in any model where it's called.

• Custom schema name macro: This macro dynamically changes the schema names depending on the environment (e.g., development or production). This helps manage environments without manually changing schema names.

-- macros/custom_schema.sql
{% macro custom_schema_name() %}
{% if target.name == 'prod' %}
'production_schema'
{% else %}
'dev_schema'
{% endif %}
{% endmacro %}

Usage in a model:

SELECT *
FROM {{ custom_schema_name() }}.orders

Here, the macro checks if the environment (target.name) is "prod" and returns the correct schema name based on that.

How to run macros:

Macros are not run directly like SQL models. Instead, they are referenced in your models or other macros and executed when the dbt project runs. For example, if you use a macro within a model, the macro will execute when you run the dbt run command.

• Calling a macro inside a model: When you include a macro in a SQL model, the macro is executed during the model's run.
• Running macros manually: You can also run certain macros manually by calling them with the dbt run-operation command. This is typically used for one-off tasks, like seeding data or performing maintenance operations.

What are the two types of tests in dbt? 

Singular tests and generic tests are the two test types in dbt: 

• Singular tests focus on specific conditions in a dbt model. If the test condition fails, it will return the rows.

Example: Let’s say you want to ensure no orders have a negative order_amount. You can write a singular test in the tests directory as follows:

-- tests/no_negative_order_amount.sql
SELECT *
FROM {{ ref('orders') }}
WHERE order_amount < 0

If this test fails, dbt will return all rows from the orders table where the order_amount is negative.

• Generic tests are predefined tests that accept arguments. They are further divided into the following types: 

Example: You can easily apply a generic test to ensure that customer_id in the customers table is unique and not null by defining it in your schema.yml file:

version: 2

models:
  - name: customers
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null

In this example:

• The unique test checks that every customer_id in the customers table is unique.
• The not_null test checks that no customer_id values are missing or null.

Advanced dbt Interview Questions 

As you progress, you may encounter more complex scenarios and advanced concepts. So here are a few challenging interview questions to help you gauge your expertise and prepare for senior-level data engineering positions.

How can you make incremental models in dbt, and when would you use them?

Incremental models in dbt are used to only process new or changed data instead of reprocessing the entire dataset each time. This is especially useful when working with large datasets where rebuilding the entire model from scratch would be time-consuming and resource-intensive.

An incremental model allows dbt to append only new data (or update changed data) based on a condition, typically a timestamp column (like updated_at).

How to create an incremental model:

1. Define the model as incremental by specifying it in the model’s config:

{{ config(
    materialized='incremental',
    unique_key='id'  -- or another unique column
) }}

2. Use the is_incremental() function to filter out new or changed rows:

SELECT *
FROM source_table
{% if is_incremental() %}
WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

3. When dbt runs this model for the first time, it will process all the data. For subsequent runs, it will only process rows where updated_at is greater than the most recent value already in the model.

When to use incremental models:

• Large datasets: When you have a large table with millions or billions of rows, rebuilding the entire table during each run would be inefficient.
• Frequent updates: If your data warehouse gets frequent updates or new data, but you don’t need to reprocess the entire dataset, incremental models can significantly reduce processing time.
• Streaming data: In cases where data is streamed or updated regularly, incremental models help keep the transformations up to date without rerunning everything.

How do you use Jinja to improve your SQL code?

Jinja makes our SQL code more flexible. With Jinja, we can define reusable templates for common SQL patterns. And since the requirements keep changing, we can use Jinja's if statements to adjust our SQL queries depending on the conditions. Doing so usually improves SQL code by breaking down complex logic, which makes it easier to understand. 

For example, if you want to convert the date format from "YYYY-MM-DD" to "MM/DD/YYYY", here's a sample dbt macro for this, which we saw in a previous question:

{% macro change_date_format(column_name) %}

  to_char({{ column_name }}::date, 'MM/DD/YYYY')

{% endmacro %}

In this code example, {{ column_name }} is where a Jinja inserts the actual column name when you use the macro. It will be replaced with the actual column name during runtime. As we have seen in previous examples, Jinja uses {{ }} to show where the replacement will occur.

How would you create custom materialization in dbt? 

Here's how to create custom materialization in dbt: 

• Create the SQL file for custom materialization. 
• Next, define a materialization macro as materialization_name: 

{% materialization materialization_name, default -%}

• Use dbt's predefined macros adapter.get_relation to set up the target table. This is where data will be loaded. 
• Now, define and execute the SQL commands to create and load data into the tables: 

{% set sql %}
    SELECT * FROM {{ ref('your_source_table') }}
    WHERE your_conditions = true
{% endset %}
{{ adapter.execute(sql) }}

• In the end, return the target relation to update dbt’s cache and optimize query execution. 

{{ return(target_relation) }}
{% endmaterialization %}

How can you debug your dbt models? Tell us about two ways. 

Here are two ways to debug our dbt models: 

1. Access the compiled SQL files in the target folder to identify and track errors.

When you run a dbt project, dbt compiles your models (written using Jinja templating) into raw SQL queries and saves them in the target directory. This compiled SQL is exactly what dbt runs against your data platform, so reviewing these files can help you identify where issues are occurring:

• Run your dbt models (e.g., dbt run or dbt test).
• Navigate to the target/compiled/ folder in your dbt project directory.
• Open the compiled SQL file for the model you're debugging. The file will contain the raw SQL dbt executed, including all transformations from the Jinja macros and references.
• Copy the compiled SQL query and run it directly in your data platform SQL editor (e.g., Postgres, BigQuery) to get detailed error messages or see the query's actual behavior.

2. Use dbt Power User Extension for VS Code to review query results directly.

The dbt Power User Extension for Visual Studio Code (VS Code) is a helpful tool for debugging dbt models. This extension allows you to review and test your queries directly within your IDE, reducing the need to switch between dbt, the terminal, and your database.

How does the dbt compile queries? 

dbt compiles queries through the following steps:

1. Parsing: dbt reads all SQL files, YAML configs, and macros in the project.
2. Context creation: It builds a context for each model, including configs and available macros.
3. Jinja rendering: It then processes SQL files as Jinja templates to replace tags and expressions with evaluated results.
4. SQL compilation: Pure SQL queries are generated for each model.
5. Artifact generation: Compiled SQL is saved in the target/compiled directory.
6. Execution preparation: For dbt run, queries are prepared for execution, potentially with additional wrapping.

This process transforms modular, templated SQL into executable queries specific to your data warehouse. We can use dbt compile or check the target/compiled directory to view and debug the final SQL for each model.

Data Warehousing and Integration-Based dbt Interview Questions

Most data engineers' jobs revolve around building and integrating data warehouses with dbt. Questions related to these scenarios are very common in interviews — that’s why I've compiled the most commonly asked ones: 

Explain three advantages of integrating dbt with Airflow. 

Integrating dbt with Airflow helps build a streamlined data pipeline. Here are some of its advantages: 

• ETL process: Airflow manages the extraction and loading of data, ensuring dbt can focus on the transformation step, resulting in a smoother overall workflow.
• Automation of dbt tasks: Airflow automates the scheduling and execution of dbt models, reducing manual intervention and improving the efficiency of your data transformations.
• Parallel task execution: Airflow allows tasks to run in parallel, enabling the processing of large datasets without compromising performance, which helps maintain fast and reliable data pipelines. 

What is the semantic layer architecture of dbt? 

The semantic layer of dbt allows us to translate raw data into the language we understand. We can also define metrics and query them with a command line interface (CLI). 

This allows us to optimize the cost as data preparation takes less time. In addition, everyone works with the same data definitions because it makes metrics consistent across the organization. 

dbt and the semantic layer. Image source: dbt

If you are using BigQuery, is dbt an unnecessary layer of data transformation? 

While BigQuery is quite helpful and can handle many transformations natively, dbt may still be necessary. Here's why:

• dbt allows you to version control your transformations, which isn't natively supported in BigQuery.
• dbt provides built-in testing frameworks and documentation generation which enhances data quality and understanding.
• dbt's ref() function and macros allow more modular and reusable SQL code.
• dbt makes managing multiple environments (dev, test, prod) in BigQuery easier.
• dbt provides a cohesive way to manage dependencies between transformations.

Does dbt provide data security? 

dbt comes in two versions: dbt Core and dbt Cloud, as seen in a previous question. dbt Core is open source and serves as a free version. That's why it does not offer any built-in security feature, and users are responsible for its deployment and security. 

However, dbt Cloud is designed to provide complete security. It complies with HIPAA and other common frameworks to ensure no privacy is harmed. So, depending on our needs, we must choose a dbt version that suits our business compliance needs.

How can you optimize the performance of dbt transformations on large datasets?

Optimizing dbt transformations for large datasets is critical for improving performance and reducing costs, especially when dealing with cloud-based data warehouses like Snowflake, BigQuery, or Redshift. Here are some key techniques to optimize dbt performance:

1. Use incremental models

Incremental models allow dbt to only process new or updated data instead of reprocessing the entire dataset every time. This can significantly reduce run times for large datasets. This process limits the amount of data processed, speeding up transformation times.

2. Leverage partitioning and clustering (for databases like Snowflake and BigQuery)

Partitioning and clustering large tables in databases like Snowflake or BigQuery help improve query performance by organizing data efficiently and reducing the amount of data scanned during queries.

Partitioning ensures that only the relevant portions of a dataset are queried while clustering optimizes the physical layout of the data for faster retrieval.

3. Optimize materializations (table, view, incremental)

Use appropriate materializations to optimize performance:

• Views are useful for light transformations but are not ideal for heavy workloads.
• Tables store the data physically, improving performance but taking up more storage.
• Incremental models are the best for large datasets that receive regular updates.

4. Use LIMIT during development

When developing transformations, adding a LIMIT clause to queries is useful to restrict the number of rows processed. This speeds up development cycles and avoids working with huge datasets during testing.

5. Run queries in parallel

Leverage your data warehouse’s ability to execute queries in parallel. For example, dbt Cloud supports parallelism, which can be adjusted based on your infrastructure.

6. Use database-specific optimization features

Many cloud data warehouses provide performance optimization features like:

• BigQuery: Use materialized views or partitioned tables.
• Snowflake: Enable auto-clustering and warehouse scaling for parallel execution.

How do you optimize dbt runs in Snowflake? 

To optimize dbt runs in Snowflake:

1. Use table clustering:

{{ config(
    cluster_by = ["date_column", "category_column"]
) }}
SELECT ...

2. Leverage Snowflake's multi-cluster warehouses for parallel model execution:

models:
  my_project:
    materialized: table
    snowflake_warehouse: transforming_wh

3. Use incremental models where appropriate:

{{ config(materialized='incremental', unique_key='id') }}
SELECT *
FROM source_table
{% if is_incremental() %}
WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

These optimizations may improve the performance and cost-effectiveness of dbt runs in Snowflake.

Behavioral and Problem-Solving dbt Interview Questions 

At the end of the interview process, interviewers usually test your problem-solving skills. They may ask you questions to see how you will respond to real-life issues. So, here are a few behavioral and problem-solving questions: 

How would you manage dbt deployment across multiple environments (dev, staging, production)?

Here’s how you can manage dbt deployment across environments:

1. Environment-specific configurations

dbt allows you to define different configurations for each environment (dev, staging, and production) in the dbt_project.yml file. You can specify different settings for things like schema, database, and data warehouse configurations.

Example in dbt_project.yml:

models:
  my_project:
    dev:
      schema: dev_schema
    staging:
      schema: staging_schema
    prod:
      schema: prod_schema

In this example, dbt automatically selects the correct schema based on the target environment (dev, staging, or prod) when running the project.

2. Using the target variable

The target variable in dbt is used to define which environment you're working in (dev, staging, production). You can reference this variable in your models or macros to customize behavior based on the environment.

Example in a model:

{% if target.name == 'prod' %}
    SELECT * FROM production_table
{% else %}
    SELECT * FROM {{ ref('staging_table') }}
{% endif %}

This logic ensures that different tables or schemas are used depending on the environment.

3. Branching and Version Control

Each environment should have its own branch in version control (e.g., Git). Developers work on the dev branch, testers and analysts use staging, and only approved changes are merged into the prod branch.

4. Continuous Integration (CI) & Continuous Deployment (CD)

In production, it’s important to have an automated deployment pipeline that runs tests and validations before deploying models. In dbt Cloud, you can set up job schedules to run specific tasks based on the environment. For dbt Core, this can be achieved through CI/CD tools like GitHub Actions or Jenkins.

How do you handle version control in dbt, especially when working with multiple team members?

Version control is essential when working on dbt projects, especially in a team environment where multiple people contribute to the same codebase. Here’s how I handle version control in dbt:

1. Use Git for version control

We use Git as the primary tool for version control in our dbt projects. Each team member works on their own branch for any changes or features they are implementing. This allows for isolated development and avoids conflicts between team members working on different tasks simultaneously.

Example: I create a new feature branch like feature/customer_order_transformation when working on a new dbt model.

2. Branching strategy

We follow a Git branching strategy where:

• The dev branch is used for ongoing development and testing.
• The staging branch is used to prepare changes for production.
• The main or prod branch is reserved for the production environment.

Team members push their changes to the dev branch and open pull requests (PRs) for code reviews. Once the changes are reviewed and approved, they are merged into staging for further testing and then promoted to production.

3. Continuous integration (CI)

We have integrated a CI pipeline (e.g., GitHub Actions, CircleCI) that automatically runs dbt tests on each pull request. This ensures that any new code passes the required tests before it’s merged into the main branch. 

The CI process runs dbt run to build models and dbt test to validate the data and check for any errors or inconsistencies.

4. Resolve merge conflicts

When multiple team members make changes to the same model or file, merge conflicts can occur. To handle this, I first review the conflict markers in the code and decide which changes to keep:

• If both changes are valid, I combine them into a new version.
• If only one set of changes is correct, I keep those and discard the other.

After resolving the conflict, I run tests locally to ensure the conflict resolution did not introduce any new errors. Once confirmed, I push the resolved changes back to the branch.

5. Documentation and collaboration

We ensure that each merge or pull request includes proper documentation of the changes made. We update the auto-generated dbt documentation so that everyone on the team has a clear understanding of the new or updated models.

How would you implement dbt in an existing data pipeline?  

Here’s how I’d implement dbt in an existing pipeline:

1. Assess the current pipeline: Identify inefficiencies and areas where dbt can improve transformation processes.
2. Set up dbt: Install dbt, create a new project, and configure connections to the data warehouse.
3. Convert transformations: Migrate existing SQL or transformation logic into dbt models, ensuring modularity and clarity.
4. Add tests and documentation: Implement dbt’s built-in testing and documentation to ensure data quality and transparency.
5. Integrate with orchestration: Schedule dbt runs using existing tools like Airflow or Prefect.
6. Start small: Implement dbt on a small subset of the pipeline to validate changes before scaling up.
7. Monitor and optimize: Continuously monitor performance and optimize models as needed.

Imagine a dbt model is failing due to the "relation does not exist" error. How do you go about debugging an error like this?

When encountering a "relation does not exist" error in dbt, this typically means the model is trying to reference a table or model that hasn’t been created or is misspelled. Here’s how I would debug it:

1. Check for typos: Ensure that the table or model name is spelled correctly in the ref() function, and verify the correct model or table is being referenced.

SELECT * FROM {{ ref('orders') }}  -- Ensure 'orders' is the correct model name

1. Verify model dependencies: If your model relies on other models, check the dbt DAG (Directed Acyclic Graph) to ensure the upstream models have been built successfully before the failing model runs.
2. Run dbt in debug mode: Use dbt debug and check the logs for detailed information about what dbt tried to do and why it failed.
3. Check data platform permissions: Ensure that dbt has the proper permissions to access the referenced table or model in the data warehouse.
4. Run individual models: Try running the dependent models individually (e.g., dbt run --models orders) to verify they exist and are built correctly before the failing model.

Tips for Preparing for a dbt Interview

dbt is a new framework that is gradually improving. It may overwhelm you to keep up with the new updates while still learning the old material. That's why you should take a balanced approach and start with the core features. Once you master them, explore the additions to understand what has changed. 

I know interviews can be nerve-wracking, especially for a specialized tool like dbt. But don't worry — I've put together some tips to help you prepare and feel confident:

Master dbt features

I can't stress this enough — get comfortable with the fundamental concepts of dbt, including models, tests, documentation, and how they all fit together. A solid grasp of these basics will serve you well in technical discussions.

DataCamp has the perfect course for this: Introduction to dbt 

DataCamp also offers some beginner-friendly courses that cover other data engineering topics in-depth:

• For data engineering basics: Understanding Data Engineering 
• For database design: Database Design 
• For data warehousing basics: Data Warehousing Concepts 
• For Snowflake basics: Introduction to Snowflake 
• For data architecture: Understanding Modern Data Architecture 
• To learn ETL and ELT in Python: ETL and ELT in Python 

Get hands-on experience with dbt

Reading about something is good, but doing is even better. This is one of the most effective ways to master dbt skills. You can find a huge list of raw datasets online to do transformations and run tests. Set up your own dbt project and play around with different features. This will make you feel much more confident talking about dbt when you've actually used it.

Prepare real-world examples

Interviewers love hearing about practical applications. Can you think of a problem you've solved using dbt or how you might use it in a hypothetical scenario? Have a few of these examples ready to share. To collect some examples, you can even study multiple case studies from dbt's official site or get ideas from public dbt projects on Git. 

Conclusion 

We covered a wide spectrum of basic to advanced dbt interview questions that will help you on your job-hunting journey. By understanding how to integrate dbt with cloud data warehouses, you will be well-equipped with advanced data transformation skills, which is the core of any data integration process. 

However, learning dbt and SQL go hand in hand. So, if you're new to SQL, check out DataCamp's SQL courses.