How to Fix “Access Denied” in BigQuery

How do I fix “Access Denied” errors in BigQuery?

“Access Denied” means the current user or service account lacks the required BigQuery permissions on a project, dataset, table, or view.

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Description

Why does BigQuery return “Access Denied” errors?

BigQuery checks Identity and Access Management (IAM) before every action. If your principal lacks bigquery.tables.getData (reading) or bigquery.tables.create (writing), the platform blocks the request and surfaces an “Access Denied” error.

How do I see which permission is missing?

Open the Cloud Console → BigQuery Editor → “Query History.” Click the failed job and review the “Permission Denied” message. It names the exact permission (e.g., bigquery.jobs.create) you need to grant.

How can I grant dataset-level access quickly?

Use the SQL-like GRANT statement in BigQuery. Grant predefined roles such as bigquery.dataViewer or bigquery.dataEditor on an entire dataset so every underlying table inherits the rights.

Example — dataset access for analysts

Suppose your ecommerce analysts query the analytics dataset that houses Customers, Orders, and OrderItems. Run the following in the Cloud Shell or bq CLI:

# Grant read-only access gcloud projects add-iam-policy-binding my-project \ --member="user:analyst@example.com" \ --role="roles/bigquery.dataViewer" \ --condition="resource.name.startsWith('projects/_/datasets/analytics')"

How do I grant table-level access with SQL?

BigQuery allows fine-grained SQL control. You can grant SELECT on individual tables, ideal when sensitive customer data exists.

GRANT SELECT on a single table

GRANT SELECT ON TABLE `my-project.analytics.Customers` TO "user:marketing_analyst@example.com";

Can I share only specific columns?

Yes. Create an authorized VIEW that exposes safe columns (e.g., name, email) and grant SELECT on the view instead of the base table.

Example — masking PII in Customers

CREATE VIEW `analytics.vw_public_customers` AS SELECT id, name, email FROM `analytics.Customers`; GRANT SELECT ON TABLE `analytics.vw_public_customers` TO "group:external_partner@example.com";

Best practices to avoid future “Access Denied” issues

Use IAM groups rather than individual users, apply the least-privilege principle, and automate grants via Terraform or Deployment Manager. Always test with a non-privileged account before production rollout.

What if the service account still fails?

Confirm it holds roles/bigquery.user at the project level (needed to create jobs) and a data access role at dataset or table level. Also ensure the account’s key is current and not disabled.

Why How to Fix “Access Denied” in BigQuery is important

How to Fix “Access Denied” in BigQuery Example Usage


-- Allow app’s service account to insert orders
GRANT INSERT ON TABLE `shop.sales.Orders`
    TO "serviceAccount:app-server@shop.iam.gserviceaccount.com";

How to Fix “Access Denied” in BigQuery Syntax


GRANT { SELECT | INSERT | UPDATE | DELETE } ON TABLE `project.dataset.table`
    TO "user:user@example.com" | "group:team@example.com" | "serviceAccount:sa@project.iam.gserviceaccount.com";

GRANT `roles/bigquery.dataViewer` ON SCHEMA `project.dataset` TO "group:analysts@example.com";

# Revoke when no longer needed
REVOKE SELECT ON TABLE `project.dataset.table` FROM "user:user@example.com";

Common Mistakes

Granting project-wide Owner role. This overexposes data and violates least-privilege. Instead, assign only roles needed, such as bigquery.dataViewer on a dataset.
Omitting roles/bigquery.user for service accounts. Without it, the account cannot create query jobs even if it has table permissions. Always add bigquery.user at the project level.