How to Manage BigQuery Users Without Google Workspace

June 30, 2025

 

1. Introduction

If you’re just getting started with BigQuery and managing a data team, chances are you’ve run into this question:

“Do I need Google Workspace to control who can access my datasets?”

Good news: You don’t.
Even without Google Workspace, you can still manage user access in BigQuery safely and effectively using free Google Groups.

This is especially useful if you're part of a small team, a startup, or just experimenting with BigQuery for the first time.

BigQuery is a powerful tool, but with great power comes... well, the need to protect your data. You don’t want every user to have full access to everything. Some people should only view data, others might need to load or transform it, and a few might manage permissions.

In this article, you’ll learn:

  • How to set up user access using Google Groups (even if you don’t have Google Workspace),

  • How to assign the right roles for different types of users,

  • And how to follow best practices so your project stays clean, secure, and easy to manage.

Let’s get started with a simple example that mirrors many real-world BigQuery teams.

2. Why User Management Matters in BigQuery

BigQuery gives you the power to store and analyze massive amounts of data. But just like you wouldn’t give the keys to your entire warehouse to every employee, you shouldn’t give full access to your data warehouse to everyone on your team.

Here’s why good user management is important:

✅ Different Roles, Different Needs

Not everyone works with data in the same way:

  • A data analyst might only need to read reports and dashboards.

  • A data engineer might need to load and transform data.

  • A data warehouse engineer like you might need to manage how that data is structured and who can access it.

  • An admin might need full control to manage billing, security, and project settings.

If everyone had the same access, things could go wrong fast — like someone accidentally deleting a dataset, or worse, exposing sensitive information.

🔐 Data Security Starts with Access Control

BigQuery uses something called IAM (Identity and Access Management) to control who can do what. By setting the right access levels, you can:

  • Protect sensitive or restricted data.

  • Prevent mistakes or accidents.

  • Make sure people only see what they’re supposed to.

Think of it like giving team members the right keys to the right rooms — not the entire building.

⚙️ Better Management, Less Headache

When you organize user access from the start:

  • It’s easier to onboard new team members.

  • You avoid messy permission problems later.

  • You stay in control, even as your team or data grows.

And the best part? You can do all of this without needing a paid Google Workspace account.

Next, let’s walk through a simple example of how to organize users in a BigQuery project.

3. Scenario Overview: Sample Team Setup

To make this easier to understand, let’s imagine a simple BigQuery project with a few datasets and a few types of users. This is a common setup in many small-to-medium data teams.

🗂️ The Datasets

You have three datasets in your BigQuery project:

  • dataset1 – Contains reports and dashboards.

  • dataset2 – Stores clean, transformed data ready for analysis.

  • dataset3 – A working area for engineers to load and process raw data.

👥 The User Groups

Instead of managing access for each person one by one (which quickly becomes a nightmare), you organize users into groups based on their roles:

Group NameRole Description
AdminFull control over the project. Can manage everything.
Data AnalystCan read data in dataset1 and dataset2 only.
Data EngineerCan write to dataset3 and create new datasets.
Data Warehouse EngineerCan write to all datasets and manage user permissions.

By assigning access to the group instead of individual users, it becomes much easier to manage — especially when people join or leave the team.

This setup keeps things organized, safe, and scalable. Everyone gets just the access they need — nothing more, nothing less.

Now let’s see how you can build this kind of access control using free Google Groups — no Google Workspace required.

4. Using Free Google Groups for Access Control

Now that you have your user groups planned out, the next step is to actually create them — and here’s the cool part:

You don’t need Google Workspace to create user groups.

You can use Google Groups (the free version) to organize your team and assign permissions in BigQuery. It works just fine, as long as you set them up correctly.

🧑‍🤝‍🧑 Create a Group for Each Role

You can go to groups.google.com and create a group for each role. Here’s a suggested naming format:

Group PurposeSuggested Email
Admins[email protected]
Data Analysts[email protected]
Data Engineers[email protected]
Warehouse Engineers[email protected]

When someone joins the team, you simply add them to the relevant group — no need to touch BigQuery permissions again.

⚙️ Important Settings to Check

Free Google Groups can be used with BigQuery IAM, but you must make sure these settings are correct:

  • Who can view group members: Anyone on the web

  • Who can send email to the group: Anyone

  • Group visibility: Public

Why? Because BigQuery needs to be able to "see" the group when assigning permissions. If the group is too private, IAM won't recognize it.

✅ Tips

  • Add a short description to each group so it’s clear what it’s for.

  • Use naming conventions that are easy to recognize across your organization.

  • Test with a small group of users first before rolling it out to your entire team.

5. Assigning IAM Roles to Groups

Now that your Google Groups are set up, it’s time to give them the right access in BigQuery. This is where IAM (Identity and Access Management) comes into play.

IAM lets you define who can do what inside your BigQuery project — and by assigning roles to groups instead of individuals, you make everything easier to manage.

🔑 Where Do You Assign Access?

There are two levels where you can assign IAM roles in BigQuery:

  1. Project-level access: This is for roles that need broad access — like creating datasets or managing users.

  2. Dataset-level access: This is more specific — for roles that only need to read or write to certain datasets.

Let’s walk through the access setup based on our sample scenario:

👥 Admin Group ([email protected])

  • Level: Project

  • Role: BigQuery Admin (roles/bigquery.admin)

  • Gives full control over all datasets and configurations.

👩‍💼 Data Analyst Group ([email protected])

  • Level: Dataset (only dataset1 and dataset2)

  • Role: BigQuery Data Viewer (roles/bigquery.dataViewer)

  • Read-only access. They can query data but not change anything.

👨‍🔧 Data Engineer Group ([email protected])

  • Level:

    • Project: BigQuery User (roles/bigquery.user) → to create new datasets.

    • Dataset3: BigQuery Data Editor (roles/bigquery.dataEditor) → to load and transform data.

  • Balanced access: not too much, not too little.

🧱 Data Warehouse Engineer Group ([email protected])

  • Level:

    • All datasets: BigQuery Data Editor (roles/bigquery.dataEditor)

    • Project: Project IAM Admin (roles/resourcemanager.projectIamAdmin) → to manage user access.

  • Great for engineers who maintain the structure and user roles.

🛠️ How to Assign These Roles

You (or an admin) can assign roles using the Google Cloud Console, gcloud command line, or scripts.

Here’s a simple example using the command line:

gcloud projects add-iam-policy-binding your-project-id \
  --member="group:[email protected]" \
  --role="roles/bigquery.dataViewer"

For dataset-level access, you’ll use the bq update command with a JSON file that contains the access configuration.

6. Best Practices for Access Management

Setting up access once is great — but keeping it organized and secure over time is what really matters. Here are a few simple best practices to follow as your BigQuery project grows.

✅ 1. Use Groups, Not Individuals

Always assign roles to Google Groups — not directly to user emails. Why?

  • It’s easier to manage.

  • When someone leaves the team, you just remove them from the group.

  • You avoid having to touch IAM settings all the time.

This one habit will save you hours later.

🔐 2. Follow the Principle of Least Privilege

Give each user only the access they need — nothing more.

Example:

  • If someone just needs to view dashboards, they shouldn’t have write access.

  • If someone only works with one dataset, don’t give them project-wide permissions.

This reduces the chance of accidents or unauthorized access.

🔁 3. Review Access Regularly

Things change: people change roles, teams grow, and projects evolve.

It’s a good idea to review user access every 3–6 months to:

  • Remove inactive users

  • Spot unnecessary access

  • Keep your permissions tidy

🧪 4. Test Before You Roll Out

Start small. Assign a role to a test group and check if access works as expected.

This helps avoid surprises, especially when dealing with sensitive datasets.

🛠️ 5. Automate When Possible

If you’re comfortable with tools like Terraform or gcloud scripts, use them to:

  • Apply consistent IAM settings

  • Version-control your access setup

  • Quickly replicate environments

Even basic automation helps reduce human error and increases visibility.

📜 6. Enable Audit Logs

Google Cloud automatically tracks who accessed what — but you need to enable Cloud Audit Logs and review them if needed.

This gives you peace of mind, and is useful for:

  • Troubleshooting issues

  • Security reviews

  • Compliance requirements

Managing access in BigQuery isn’t just about safety — it’s also about clarity and control. When everyone knows their role and has the right level of access, your data workflows run smoother.

In the next section, we’ll talk about when it might make sense to upgrade to Google Workspace, especially if your team or organization is getting bigger.

7. When You Might Need Google Workspace

So far, we’ve shown how you can manage BigQuery access just fine without Google Workspace — using free Google Groups and some smart practices.

But at some point, as your team grows or your organization matures, you might start to feel the limits. That’s when Google Workspace becomes a more attractive option.

Here’s when you might consider upgrading:

🧑‍💼 1. Your Team Is Growing Fast

Managing 3–5 Google Groups manually? No problem.

But if you’re managing:

  • 20+ groups,

  • 100+ users,

  • or multiple projects with complex access patterns,

then you’ll want more control — and a central place to manage it all.

Google Workspace lets you manage users, groups, and permissions from the Admin Console, with better visibility and less manual work.

🔐 2. You Need Stronger Security Controls

Google Workspace adds:

  • Single Sign-On (SSO),

  • 2-step verification enforcement,

  • Advanced user activity tracking,

  • and tighter control over file sharing and group access.

This is helpful when you're dealing with sensitive data, external stakeholders, or need to meet compliance standards.

📜 3. You're in a Regulated Industry

If you work in finance, healthcare, or any industry with data compliance requirements (like HIPAA, GDPR, etc.), Workspace helps you stay in line with policies and audit requirements.

You also get better support and more admin features out of the box.

🔁 4. You Want Smoother Integration Across Teams

Workspace brings better integration with other Google tools:

  • Gmail + Groups

  • Google Meet + Calendar

  • Shared Drives + Access Control

It all works together — and that’s something you might value as your company grows.

🤔 So… Do You Need Workspace?

No, not at first.
If you're a solo engineer, part of a small team, or just experimenting with BigQuery, the free tools are enough.

But if you're managing critical data infrastructure at scale — or want full control and visibility — Google Workspace might be worth the investment.

Next, let’s wrap it all up with a quick summary and a few final tips.

8. Conclusion

You don’t need a fancy setup to manage access in BigQuery.

With just:

  • A free Gmail account,

  • A few well-named Google Groups, and

  • The right IAM roles,

you can create a clean, secure, and scalable access system for your BigQuery project — no Google Workspace required.

By thinking in terms of roles, assigning access to groups, and following a few best practices, you’ll avoid messy permission problems later. Even better, your team will know exactly what they can (and can’t) do — which keeps your data safe and your workflows smooth.

So if you're a beginner data warehouse engineer or just starting to manage a BigQuery environment, this is a perfect place to begin.

Start small. Keep it simple. And grow with confidence.

🎯 Next steps:

  • Create your user groups on Google Groups

  • Review the roles your team needs

  • Apply IAM settings one step at a time

Got a bigger team or compliance needs? That’s when Google Workspace might help. But until then, the free tools are more powerful than you think.

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