Analytics Engineering: Modern Data Teams

What is Analytics Engineering? Where has it come from? Why is it Important?

Welcome to the first post in a five-part blog series on Analytics Engineering! Whether you’re a data enthusiast looking to dive into this growing field or a business leader keen to understand how analytics engineers (AEs) can excel your team, these series of articles are for you. 

I’m starting with the basics, and will go into more depth as we go along! We’ll cover as many areas as we can, but I’m always happy to answer questions or chat through more. You can reach out to me directly via email or connect with me on LinkedIn.

First things first…What is Analytics Engineering?

In simple terms, analytics engineering is the bridge between raw data and actionable insights. Analytics engineers transform messy, unstructured data into clean, usable datasets that empower businesses to make smarter decisions. They create and maintain the data infrastructure that supports reporting, dashboards, and advanced analytics. 

If you’re wondering how this is different from a data analyst or data scientist, think of it this way: 

• Data analysts focus on exploring and interpreting data to answer questions – finding insights and more 

• Data scientists build complex models to predict future trends – showing what the future can look like 

• Analytics engineers, on the other hand, lay the groundwork by making data accessible, reliable, and reusable. They build data models, define business logic, and ensure data pipelines are efficient and free of problems/errors 

How Did Analytics Engineering Come About?

The rise of analytics engineering is a direct response to the rapid growth of data in the last decade. As businesses started collecting enormous amounts of data, it became clear that: 

1. Traditional tools couldn’t keep up. Manual processes and siloed workflows led to inefficiencies 
2. The role of data expanded. Teams wanted real-time insights; not static reports delivered days later 
3. Collaboration became key. Data was no longer just for analysts—sales, marketing, and product teams needed it, too 

Modern companies required a more scalable, collaborative approach to data management. Enter the analytics engineer! This role emerged to fill the gap between data engineers (who focus on infrastructure) and data analysts (who focus on insights). 

With tools like DBT (more on that in a future article), analytics engineers brought software engineering principles—like version control and testing—into the analytics space. This evolution turned what was once a slow, manual process into an efficient, scalable operation. 

Why Does Analytics Engineering Matter?

Analytics engineering is the future of data work. It combines the creativity of analysis with the technical side of engineering. It’s ideal for people who love solving problems, building scalable solutions, and collaborating with multiple teams. 

For employers: 

An analytics engineer is your key to data clarity. They ensure that every team member—whether they’re in marketing, product, or executive leadership—has access to consistent, reliable data. 

View all articles 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
Part four: The Power Trio: DBT, Looker, and Snowflake in Analytics Engineering
Part five: Here’s Why Companies Should Hire Analytics Engineers