Why project structure scales with team size in dbt - Why It Works This Way

Overview - Why project structure scales with team size

What is it?

Project structure is how a data project is organized into folders, files, and components. As more people join a team, organizing the project well helps everyone work together smoothly. Without a clear structure, team members can get confused, overwrite each other's work, or waste time searching for things. Good project structure grows with the team to keep work efficient and clear.

Why it matters

When many people work on the same data project, a messy or unclear structure causes delays, mistakes, and frustration. A well-planned structure helps teams avoid conflicts, share work easily, and maintain quality. Without it, projects become chaotic, slowing down decision-making and reducing trust in data results. This impacts business decisions and can cost time and money.

Where it fits

Before understanding project structure, learners should know basic dbt concepts like models, tests, and sources. After mastering structure, they can learn advanced topics like modular design, deployment pipelines, and team collaboration tools. This topic connects foundational dbt skills to real-world teamwork and scaling.

Mental Model

Core Idea

A clear project structure acts like a well-organized office where each team member knows where to find and place their work, enabling smooth collaboration as the team grows.

Think of it like...

Imagine a kitchen where many chefs cook together. If all ingredients and tools are scattered randomly, cooking becomes chaotic. But if everything has a labeled place and stations are assigned, chefs can work side by side without bumping into each other or wasting time.

Project Structure
┌───────────────────────────────┐
│ Root Folder                   │
│ ├── models/                  │
│ │   ├── staging/             │
│ │   ├── marts/               │
│ │   └── intermediate/        │
│ ├── tests/                   │
│ ├── macros/                  │
│ ├── snapshots/               │
│ └── docs/                    │
└───────────────────────────────┘

Team Members
┌─────────────┐  ┌─────────────┐  ┌─────────────┐
│ Analyst 1   │  │ Analyst 2   │  │ Analyst 3   │
│ works in    │  │ works in    │  │ works in    │
│ staging/    │  │ marts/      │  │ macros/     │
└─────────────┘  └─────────────┘  └─────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding basic dbt project layout

Concept: Learn the default folders and files in a dbt project and their purposes.

A dbt project has folders like models, tests, macros, and snapshots. Models contain SQL files that define data transformations. Tests check data quality. Macros are reusable SQL snippets. Snapshots track changes over time. This basic layout helps organize code logically.

Result

You can identify where to put new models, tests, or macros in a dbt project.

Knowing the default layout is essential before customizing or scaling the project structure.

2

Foundation

Recognizing team roles and work overlap

Concept: Understand how multiple people working on the same project can cause conflicts without structure.

When one person works alone, they can keep all files in one folder. But with multiple people, if everyone edits the same files or folders without rules, changes can overwrite each other. This causes confusion and errors.

Result

You see why a simple project layout is not enough for teams.

Realizing that team size affects project complexity motivates the need for better structure.

3

Intermediate

Organizing models by function and domain

🤔Before reading on: do you think grouping models by data source or by business domain works better for teams? Commit to your answer.

Concept: Learn to group models into folders by their role or business area to reduce conflicts and improve clarity.

Models can be grouped into folders like staging (raw data cleaning), marts (business logic), and intermediate (shared transformations). Alternatively, grouping by business domain (sales, marketing) helps teams focus on their area. This separation helps team members work independently.

Result

A clearer folder structure that reduces overlap and makes it easier to find models.

Understanding grouping strategies helps teams divide work and avoid stepping on each other's toes.

4

Intermediate

Using naming conventions and documentation

🤔Before reading on: do you think consistent file names and docs help only new team members or everyone? Commit to your answer.

Concept: Introduce consistent naming and documentation to make the project easier to navigate and maintain.

Using clear, consistent file names like 'stg_customers.sql' or 'mart_sales.sql' helps everyone know what each file does. Adding documentation inside files or in a docs folder explains purpose and usage. This reduces confusion and speeds onboarding.

Result

A project where team members quickly understand each other's work and find files easily.

Knowing that naming and docs benefit all team members improves collaboration and reduces errors.

5

Intermediate

Implementing modularity with macros and snapshots

Concept: Learn how to reuse code and track data changes to support team scaling.

Macros let teams write reusable SQL snippets to avoid duplication. Snapshots track how data changes over time, useful for audits. Organizing these in dedicated folders helps teams share and maintain code efficiently.

Result

Less duplicated code and better data history tracking across the team.

Recognizing reusable components and data versioning as key to scalable projects prevents messy, duplicated work.

6

Advanced

Managing dependencies and build order

🤔Before reading on: do you think dbt automatically handles model build order perfectly without structure? Commit to your answer.

Concept: Understand how dbt builds models in order based on dependencies and how structure affects this.

dbt uses model references to build models in the right order. A clear structure with logical grouping helps avoid circular dependencies and build errors. Teams can better plan changes and test impacts.

Result

Reliable model builds and easier troubleshooting of dependency issues.

Knowing how structure influences build order helps prevent complex bugs and downtime in production.

7

Expert

Scaling structure with multiple teams and environments

🤔Before reading on: do you think one project structure fits all team sizes and environments? Commit to your answer.

Concept: Explore how large organizations use multiple dbt projects, environments, and deployment strategies to scale.

Big teams split work into multiple dbt projects or repositories by domain or function. They use environments like dev, staging, and prod to test changes safely. CI/CD pipelines automate deployments. This complex structure supports many contributors without conflicts.

Result

A robust, scalable system that supports many teams working in parallel with minimal friction.

Understanding multi-project and environment strategies reveals how large companies maintain data quality and speed at scale.

Under the Hood

dbt organizes SQL files into folders that the tool reads to build a Directed Acyclic Graph (DAG) of model dependencies. When you run dbt, it compiles SQL models in dependency order, ensuring data flows correctly. The folder structure and naming conventions help dbt and team members understand and manage these dependencies. As teams grow, clear separation prevents overlapping edits and circular dependencies.

Why designed this way?

dbt was designed to be simple for individuals but powerful for teams. Early versions had flat structures, but as users scaled, the need for modularity and clear organization became clear. The folder-based structure with conventions balances flexibility and order, allowing teams to customize while maintaining clarity. Alternatives like monolithic scripts or no structure led to chaos in team environments.

┌───────────────┐
│ dbt Project   │
│ ┌───────────┐ │
│ │ models/   │ │
│ │ ┌───────┐ │ │
│ │ │ stg/  │ │ │
│ │ └───────┘ │ │
│ └───────────┘ │
│               │
│ Dependency    │
│ Graph (DAG)   │
│ ┌───────────┐ │
│ │ Model A   │ │
│ │   ↓       │ │
│ │ Model B   │ │
│ └───────────┘ │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does a flat project structure work well for large teams? Commit yes or no.

Common Belief: A simple flat folder with all models together is fine regardless of team size.

Reality: Flat structures cause confusion and conflicts as teams grow, making collaboration inefficient.

Why it matters: Ignoring structure leads to overwritten work, longer debugging, and slower project progress.

Quick: Do naming conventions only help new team members? Commit yes or no.

Common Belief: Naming conventions are only useful for onboarding new people.

Reality: Consistent naming helps all team members quickly understand and find code, improving daily productivity.

Why it matters: Without conventions, even experienced members waste time searching and risk mistakes.

Quick: Does dbt automatically prevent all dependency errors? Commit yes or no.

Common Belief: dbt's dependency system means you don't need to worry about project structure.

Reality: Poor structure can cause circular dependencies and build failures despite dbt's system.

Why it matters: Assuming dbt handles everything leads to fragile projects and unexpected errors.

Quick: Is one project structure ideal for all teams and environments? Commit yes or no.

Common Belief: A single project structure fits all team sizes and deployment environments.

Reality: Large organizations need multiple projects and environments to manage complexity and risk.

Why it matters: Using one structure everywhere causes bottlenecks and risks in production.

Expert Zone

1

Large teams often create sub-projects or packages to isolate domains, reducing cross-team conflicts.

2

Naming conventions can encode metadata like freshness or owner, aiding automation and accountability.

3

Environments (dev, staging, prod) require separate configurations and sometimes duplicated structures for safe testing.

When NOT to use

For solo projects or very small teams, complex multi-folder structures add unnecessary overhead. Instead, a simple flat layout with minimal folders is faster and easier. Also, if rapid prototyping is needed, strict structure can slow down experimentation.

Production Patterns

In production, teams use CI/CD pipelines to test and deploy dbt projects automatically. They split projects by business domain, assign ownership, and enforce naming and documentation standards. Monitoring tools track build times and failures, helping maintain quality as teams scale.

Connections

Software Engineering Project Structure

Similar pattern of organizing code and resources to support team collaboration and scaling.

Understanding software project organization helps grasp why dbt projects need modularity and clear boundaries.

Agile Team Collaboration

Project structure supports agile workflows by enabling parallel work and clear ownership.

Knowing agile principles clarifies why structure must evolve as teams grow and work becomes more complex.

Urban City Planning

Both involve organizing spaces and pathways to support many users efficiently and avoid chaos.

Seeing project structure like city planning reveals the importance of clear zones, routes, and rules for smooth operation.

Common Pitfalls

#1 Putting all models in one folder regardless of function or domain.

Wrong approach:

models/
customers.sql
sales.sql
marketing.sql
orders.sql

Correct approach:

models/
staging/
stg_customers.sql
stg_orders.sql
marts/
mart_sales.sql
mart_marketing.sql

Root cause:

Not recognizing that grouping by function or domain reduces conflicts and improves clarity.

#2 Using inconsistent or unclear file names.

Wrong approach:

models/
cust.sql
sales_data.sql
marketing1.sql

Correct approach:

models/
staging/
stg_customers.sql
marts/
mart_sales.sql
mart_marketing.sql

Root cause:

Underestimating how naming conventions help navigation and reduce errors.

#3 Ignoring dependency errors caused by circular references.

Wrong approach:

Model A references Model B, and Model B references Model A without clear separation.

Correct approach:

Refactor models to remove circular dependencies by splitting logic or using intermediate models.

Root cause:

Assuming dbt automatically resolves all dependency issues without careful structure.

Key Takeaways

Project structure is essential for smooth collaboration as data teams grow in size.

Organizing models by function or domain reduces conflicts and makes work clearer.

Consistent naming and documentation help all team members find and understand code quickly.

Understanding dbt's dependency system and build order prevents errors and downtime.

Large teams benefit from multi-project setups and environments to manage complexity and risk.