The Power Trio: DBT, Looker, and Snowflake in Analytics Engineering

Welcome back to my Analytics Engineering series. Today, I’m looking at one of the most popular tech stacks, DBT, Looker, and Snowflake. These tools have become the backbone of modern data workflows, making analytics more scalable, efficient, and collaborative.

Whether you’re a candidate keen to understand industry-leading tools or a business looking to future-proof your data stack, knowing how these three work together is key.

Why DBT, Looker, and Snowflake?

DBT, Looker, and Snowflake form a powerhouse analytics stack. Snowflake stores raw data, DBT transforms it, and Looker visualises insights. Together, they create a scalable, efficient, and collaborative solution for modern data teams.

To put it simply, Snowflake stores and queries raw data; DBT transforms data into clean, reusable models; and Looker visualises the data and turns it into insights.

Breaking It Down

1. Snowflake: The Cloud Data Warehouse

Snowflake is where all your data lives; fast, scalable, and cost-effective.

• Why Snowflake?
  • Cloud-native with separate compute and storage
  • Pay only for what you use
  • Handles large-scale queries effortlessly
• Key Features:
  • Query data directly using SQL
  • Automatically scales with workload demand
  • Seamlessly integrates with DBT and Looker
• Example:
  • If you’re pulling millions of rows from CRMs and ad platforms daily, Snowflake stores and processes it efficiently, letting you focus on insights rather than infrastructure.

2. DBT: Data Transformation Made Easy

DBT (Data Build Tool) transforms raw data into clean, structured datasets using SQL.

• Why DBT?
  • Brings software engineering best practices to data
  • Makes transformations modular, testable, and version-controlled
• Key Features:
  • SQL-first approach; no need to learn a new language
  • Built-in testing to catch errors early
  • Modular workflows for reusable, well-structured data models
• Example:
  • Need to merge customer purchase data from multiple sources? DBT transforms it into a single, reliable dataset, with tests ensuring accuracy.

3. Looker: Data Visualisation and BI

Looker turns transformed data into dashboards and reports that business users can explore.

• Why Looker?
  • Empowers teams to find answers without relying on analysts
  • Provides live insights by querying Snowflake directly
• Key Features:
  • LookML ensures consistent business logic across reports
  • Drill-down capabilities let users explore data without breaking anything
  • Connects directly to Snowflake for real-time insights
• Example:
  • Your sales team wants to track revenue trends. Looker provides a live dashboard powered by Snowflake and DBT transformations.

How These Tools Work Together

Snowflake – Data Storage

1. • Raw data flows into Snowflake from CRMs, ad platforms, and application logs
   • Snowflake stores and processes large-scale queries

DBT – Data Transformation

1. • DBT turns raw data into clean, structured datasets
   • Encodes business rules (e.g., defining “active customers”) and applies data tests

Looker – Data Visualisation

1. • Looker connects to Snowflake to visualise transformed data
   • Teams access real-time insights via dashboards and reports

The Benefits of This Tech Stack

For analytics engineers, the combination of DBT, Looker, and Snowflake simplifies workflows and significantly reduces the need for manual data wrangling. By automating transformations and providing a structured approach to data modelling, engineers can spend less time managing pipelines and more time delivering valuable insights. This stack also enhances collaboration between analysts, engineers, and business teams, ensuring that data processes are transparent and efficient. Instead of worrying about infrastructure challenges, analytics engineers can focus on refining data models, optimising queries, and uncovering trends that drive decision-making.

For companies, this tech stack provides a scalable and future-proof solution for data management. As businesses grow, their data needs evolve, and this stack ensures that performance and efficiency scale alongside demand. It also reduces errors by enforcing standardised transformations and automated testing, ensuring that teams across the organisation work with consistent, reliable metrics. With real-time insights readily available, businesses can make faster, data-driven decisions, enabling them to respond proactively to market changes, customer behaviour, and operational challenges.

DBT, Looker, and Snowflake have become the go-to stack because they balance flexibility, scalability, and ease of use. Companies of all sizes rely on it to:

• Deliver faster insights
• Reduce technical debt
• Improve collaboration across teams

What’s Next?

Whether you’re an aspiring analytics engineer  or a company looking to modernise your data stack, mastering these tools is a game-changer. Start experimenting with DBT, Looker, and Snowflake today and see how they can transform your data workflows! There are many resources and courses, from LinkedIn Learning, to Udemy or Coursera, to seeing if your company can support you in a course to expand your knowledge!

In my next article, we’ll explore emerging trends in analytics engineering and how to stay ahead in this fast-moving field.

View the previous article in this series:

Part one: Analytics Engineering: Modern Data Teams
Part two: The Rise of Analytics Engineers: The Modern Data Stack
Part three: Key Skills and Tools for Analytics Engineers: Building the Future of Data