8 Essential Tips every dbt Developer should know: Level Up Your dbt Development

📘 Introduction

dbt gives you the power to transform raw data into clean, trusted models. But writing SQL is just the starting point. To truly get value from dbt, you need practices that keep your code organized, your models reliable, and your team moving fast. Whether you're a beginner or a experienced analytics engineer, these 8 tips will help you build cleaner, smarter, and more scalable dbt projects.

🔗1️⃣ Use source() and ref()

Always use ref() when referencing other dbt models and source() when referencing raw data sources. This ensures your dependency graph is accurate, enables lineage tracking, and lets dbt manage execution order intelligently.

📦2️⃣ Use dbt packages

Leverage open-source packages like dbt_utils, dbt_expectations, and dbt_artifacts to supercharge your workflow. These packages offer prebuilt macros, tests, and features that save time and promote best practices.

🧩3️⃣ Modularize with CTEs and Jinja

Break your SQL models into clean, readable Common Table Expressions (CTEs). Use Jinja macros to abstract repetitive logic and improve code reusability across models.

✅4️⃣ Use dbt’s built-in tests

Validate your models with built-in tests like not_null, unique, and relationships. Combine these with custom tests using packages like dbt_utils to catch data quality issues early and build trust in your pipelines.

📝5️⃣ Document As You Go