Why project structure scales with team size in dbt

Introduction

Good project structure helps teams work together smoothly. As teams grow, clear organization keeps work easy to find and reduces mistakes.

When more people join a data project and need to share work without confusion

When different team members focus on different parts of data models or reports

When you want to avoid overwriting each other's work in a shared project

When you want to speed up onboarding new team members by having clear folders and files

When managing complex data transformations that need clear separation

Syntax

dbt

No strict code syntax applies here, but a typical dbt project structure looks like:

project_root/
  ├── models/
  │    ├── staging/
  │    ├── marts/
  │    └── core/
  ├── macros/
  ├── tests/
  ├── snapshots/
  └── dbt_project.yml

Organize models into folders by purpose, like staging for raw data, marts for business logic.

Use clear naming so team members know where to add or find code.

Examples

Separate raw data preparation (staging) from business-focused models (marts).

dbt

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

Core models hold essential tables; marts build on them for specific reports.

dbt

models/
  core/
    customers.sql
    products.sql
  marts/
    sales_summary.sql

Sample Program

This code creates a simple dbt project folder structure in a temporary directory and prints it out. It shows how organizing files helps teams find and work on parts easily.

dbt

import os

def list_dbt_project_structure(root):
    for folder, subfolders, files in os.walk(root):
        level = folder.replace(root, '').count(os.sep)
        if level == 0:
            continue
        indent = ' ' * 2 * (level - 1)
        print(f'{indent}{os.path.basename(folder)}/')
        subindent = indent + '  '
        for f in files:
            print(f'{subindent}{f}')

# Simulate a small dbt project structure
project = {
    'models': {
        'staging': ['customers.sql', 'orders.sql'],
        'marts': ['sales.sql', 'marketing.sql']
    },
    'macros': [],
    'tests': [],
    'snapshots': []
}

# Create folders and files in a temp directory
import tempfile
import pathlib

temp_dir = tempfile.TemporaryDirectory()
root_path = pathlib.Path(temp_dir.name)

for folder, subfolders in project.items():
    folder_path = root_path / folder
    folder_path.mkdir()
    if isinstance(subfolders, dict):
        for subfolder, files in subfolders.items():
            subfolder_path = folder_path / subfolder
            subfolder_path.mkdir()
            for file in files:
                (subfolder_path / file).write_text('-- SQL code')
    else:
        # empty folders
        pass

list_dbt_project_structure(str(root_path))

temp_dir.cleanup()

Important Notes

As teams grow, clear folder names prevent confusion about where to add or find models.

Consistent structure helps automated tools and tests run smoothly.

Regularly review and update structure as project needs change.

Summary

Good project structure keeps team work organized and efficient.

Separate models by purpose to reduce conflicts and confusion.

Clear structure helps new team members learn and contribute faster.