The Rise of Analytics Engineers: The Modern Data Stack

Welcome back to my series on Analytics Engineering! If you’re new, you might want to check out our first article, where we broke down what analytics engineering is all about. In this one, we’re diving deeper into the rise of analytics engineers—why this role is upcoming and rising to take centre stage and fits into the modern data stack.

Why Are Analytics Engineers in Demand?

Over the past few years, we’ve seen a shift in how companies think about and use data. It’s different from the days when a few SQL queries and a monthly dashboard were enough to satisfy business needs and stakeholder requirements. Data is becoming more important and as a result organisations want fast, reliable, and consistent insights, and that’s where analytics engineers come in.

Here are a few reasons this role has boomed:

1. The Explosion of Data
Businesses are generating more data than ever—think customer interactions, fast paced fintech’s and social media etc. Managing and extracting value from this boom of information requires a structured approach, which analytics engineers excel at.

2. The Evolution of Data Teams
Traditional data teams often operated in silos:

• • Data engineers focused on pipelines, architecture and infrastructure.
  • Data analysts answered questions using pre-existing data & generating insights and stories.

But we’ve noticed gaps emerging, which means companies at the forefront have started evolving. Some – not all – noticed a gap in who would ensure the data is clean, well-documented, and usable for analysis – not on the governance and management side, more as a gap between analysts and engineers. That brought us to the analytics engineer which acts as a hybrid role that combines technical expertise with business context to bridge this divide.

3. The Modern Data Stack
The modern data stack is all about leveraging cloud-first, modular tools to scale data workflows. Tools like Snowflake, Fivetran, and DBT make it easier than ever to manage data, but they also require someone to utilise and maintain them. Analytics engineers are the glue that holds the modern data stack together.

4. Collaboration is Key
Today’s data teams don’t work in isolation. Analytics engineers collaborate closely with product, marketing, finance, and leadership to ensure everyone speaks the same “data language.” Their work ensures that dashboards and reports pull from a single source of truth, preventing chaos or confusion.

How Analytics Engineers Fit into the Modern Data Stack

I’ll break it down below:

 1. Data Integration and Transformation

Many tools make it easier to pull raw data from various sources (e.g., CRMs, ad platforms, internal systems). But raw data is messy—it’s full of inconsistencies, duplicates, and irrelevant information.

Analytics engineers use tools like DBT (Data Build Tool) to clean, transform, and organise this data into models that are easy to query. Think of it as turning unpolished rocks into sparkling diamonds.

2. Version Control and Testing

Analytics engineers sometimes lend aspects from software engineering, and the more technical side, meaning they can use things like Git to track changes in data models; automated testing to catch errors – before business impact – and ensuring reliable data and strong dashboard.

Borrowing principles from software engineering, analytics engineers implement.

3. Scalability and Documentation

As companies grow, so does the complexity of their data. Analytics engineers design scalable data workflows that can handle increasing volumes, and they document everything, making it easy for new team members (or other teams) to hop in and understand the data.

4. Empowering Analysts and Stakeholders

To clarify, Analytics Engineers do not replace your existing data teams – they help the data team and stakeholders. By handling the heavy lifting of data transformation, analytics engineers help data analysts to focus on generating insights, not wrangling data. Stakeholders across the company benefit from faster, more accurate reporting, enabling better decision-making. Everyone can run in a smoother, faster way!

Why This Role Is the Future

Analytics Engineers are uniquely positioned to thrive in our data-driven world. They combine the precision of engineers with the curiosity of analysts, making them indispensable in modern organisations.

For Data Professionals:
This is an exciting time to join the field. The demand for analytics engineers is growing rapidly, and the role offers opportunities to work with cutting-edge tools, collaborate across teams, and make a tangible impact on the business but make sure you can utilise the stack!

Investing in analytics engineering ensures your data infrastructure is solid, scalable, and ready for the future. Whether you’re scaling up or just starting your data journey, an analytics engineer is key to getting the most value from your data stack.

What’s Next?

In the next post, I’ll explore the key skills and tools every analytics engineer should know, from foundational expertise in SQL to the latest innovations like DBT and cloud-based platforms like Snowflake, creating a beautiful tech stack for Analytics Engineering.

Keep an eye out on the next blog post if you’re curious about how analytics engineering can transform your team—or your career! If you want help building your team, reach out with any questions and I can support.