Securing Snowflake Credentials in dbt Cloud

Keep Snowflake secrets safe in dbt Cloud

Learn step-by-step techniques—service accounts, OAuth, scoped roles, environment variables, and secret managers—to secure Snowflake credentials while developing, testing, and deploying dbt projects.

Definition

Securing Snowflake credentials in dbt Cloud is the practice of storing and transmitting usernames, passwords, OAuth tokens, or key-pair secrets so that they are never exposed to unauthorized users or systems during dbt development, testing, and production runs.

Why Securing Snowflake Credentials in dbt Cloud Matters

dbt Cloud automates model builds, tests, and deployments. Each run needs to log in to your Snowflake warehouse. If credentials leak, attackers can access data, rack up compute bills, or tamper with analytics. A single compromised password can cascade across environments because dbt Cloud often executes with elevated roles. Correct credential management therefore protects:

• PII and commercially sensitive data in Snowflake
• Warehouse credits that could be consumed by malicious queries
• The integrity of production models that feed BI dashboards and ML pipelines
• Compliance with SOC 2, HIPAA, GDPR, or internal security policies

How Credential Management Works in dbt Cloud

1. Connection Methods Overview

dbt Cloud supports three authentication patterns for Snowflake:

1. Username & Password (legacy)—simple but least secure.
2. OAuth—delegates auth to Snowflake-supported identity providers (Okta, Azure AD, Google) and removes static passwords.
3. Key-pair Authentication—service account uses an RSA key instead of a password and can be rotated programmatically.

2. Secret Storage in dbt Cloud

Within the dbt Cloud UI you define an Environment. Each environment has a Connection object that stores credentials in an encrypted database using AWS KMS (for US-hosted accounts) or Google KMS (for EU). Secrets never appear in the REST API and can be viewed only by users with the Manage Environments permission.

3. Environment Variables

For maximum control you can omit credentials from the UI entirely and load them at runtime through Environment Variables. dbt Cloud injects variables into the execution container where profiles.yml can reference them via Jinja:

{% raw %}
outputs:
prod:
type: snowflake
account: "{{ env_var('SNOWFLAKE_ACCOUNT') }}"
user: "{{ env_var('SNOWFLAKE_USER') }}"
role: "{{ env_var('SNOWFLAKE_ROLE') }}"
database:"ANALYTICS"
warehouse:"TRANSFORMING"
authenticator: externalbrowser # if using OAuth
{% endraw %}

Variables are encrypted at rest and masked in logs.

4. Least-Privilege Role Design

Even a perfectly encrypted secret is dangerous if it unlocks a SYSADMIN role. Follow Snowflake’s RBAC model:

• Create a dedicated DBT_CLOUD_ROLE granting only USAGE on warehouse, database, schemas, and CREATE/MODIFY on objects dbt owns.
• Use separate roles for development (DBT_DEV_ROLE) and production (DBT_PROD_ROLE) environments.
• Revoke the role from human users to enforce automations only.

5. Key Pair Rotation & Secrets Managers

Automate security tasks with your platform’s secrets manager (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager):

1. Store the RSA private key or OAuth refresh token in the vault.
2. Create a short-lived access token for dbt Cloud via CI/CD that calls the vault and sends the secret to the dbt Cloud REST API.
3. Rotate keys every 30–90 days and audit via the vault’s rotation logs.

6. Audit & Monitoring

Combine dbt Cloud job run logs with Snowflake’s ACCOUNT_USAGE views (LOGIN_HISTORY, QUERY_HISTORY) to verify that only expected roles are logging in from dbt Cloud IP ranges. Enable Snowflake ALERTs or an external SIEM to trigger notifications on anomalies.

Practical Walk-Through: Production Deployment with Key-Pair Auth

1. Create a Snowflake DBT_PROD user:

CREATE USER DBT_PROD PASSWORD = 'temporary' DEFAULT_ROLE = DBT_PROD_ROLE MUST_CHANGE_PASSWORD = TRUE;

2. Generate a 2048-bit RSA key pair locally:

ssh-keygen -t rsa -b 2048 -m PEM -f dbt_prod_key -N ""

3. Upload the dbt_prod_key.pub to Snowflake:

alter user DBT_PROD set rsa_public_key = 'MIIBIjANBgkqh...';

4. Store the dbt_prod_key private key in AWS Secrets Manager.
5. Configure your CI pipeline to fetch the private key, then call the dbt Cloud API /accounts/<id>/jobs/<id> endpoint to update the environment variable SNOWFLAKE_PRIVATE_KEY.
6. In profiles.yml reference the key:

{% raw %}
private_key_path: "{{ env_var('SNOWFLAKE_PRIVATE_KEY_PATH') }}"
private_key_passphrase: "{{ env_var('SNOWFLAKE_PRIVATE_KEY_PASSPHRASE') }}"
{% endraw %}

7. Kick off a dbt Cloud run; Snowflake will audit a key-pair login from DBT_PROD only.

Best Practices Checklist

• Prefer OAuth or key-pair auth; avoid passwords.
• Enforce least privilege with Snowflake roles.
• Use environment variables and secret managers for runtime injection.
• Rotate keys/tokens and monitor usage.
• Separate development and production credentials.

Common Mistakes and How to Avoid Them

Storing Credentials in profiles.yml Committed to Git

Why it’s wrong: Anyone with repo access can read secrets.
Fix: Git-ignore profiles.yml and inject values via environment variables.

Using an Over-Privileged Snowflake Role

Why it’s wrong: If dbt’s role has ACCOUNTADMIN, a leak is catastrophic.
Fix: Create a scoped service role with only the grants dbt needs.

Failing to Rotate Keys

Why it’s wrong: Static keys accumulate risk over time.
Fix: Automate rotation with your cloud secrets manager and dbt Cloud API.

Where Galaxy Fits In

While Galaxy is primarily a modern SQL editor rather than a deployment platform, many teams pair Galaxy for interactive query authoring with dbt Cloud for scheduled transformations. Galaxy never stores warehouse credentials on its servers; all connections remain local in the desktop app’s encrypted keychain. When you paste the same Snowflake service account details that dbt Cloud uses, Galaxy respects your existing RBAC model—so you can explore data quickly without creating a second set of secrets.

Conclusion

Securing Snowflake credentials in dbt Cloud is equal parts technical configuration and operational discipline. By combining secret managers, environment variables, least privilege roles, and routine auditing, you can reap the productivity benefits of dbt Cloud while keeping your Snowflake account locked down.