Over the last few years we’ve evolved the way our analytics team works to enable easy access to accurate and reliable data for faster, better decision-making. Recently we made one more change—our Business Intelligence Analysts are now Analytics Engineers!

This might sound like a big step but in reality it recognises a few years of strategic changes to increase productivity and impact. The old title didn’t reflect the team’s current focus on enabling others by building and maintaining our analytics platform, working alongside our data platform and data science teams. In this blog post I’ll describe how we got here and why we believe Analytics Engineering is the way to go.

Several Ages of Analytics

Looking back over the last twelve years or so, I’ve seen three distinct phases in how we used data for business decision making at FreeAgent:

1. The Hand Crank Era

Low Productivity, Variable Impact

In these early days, every analytics request landed on the desk of a single person or small team who tackled each question from scratch. I’ve been that person—digging through unfamiliar datasets, creating one-off analyses, and struggling to scale as demand grew. While some insights proved valuable, the approach wasn’t sustainable as our business expanded.

2. The Tool Enhancement Phase

Medium Productivity, Medium Impact

As we matured, we built our first data warehouse and implemented basic BI tools. This allowed our growing analytics team to increase their productivity by creating standardized reports for colleagues across the business. However, impact remained constrained because most staff couldn’t modify these reports without SQL knowledge, creating a persistent bottleneck.

3. The Business Empowerment Age

High Productivity, High Impact

For the past five years, we’ve been transitioning to a model that truly delivers on our mission: enabling easy access to accurate, reliable data for faster, better decision-making. This shift required four key developments:

• Implementing an intuitive data model with clear definitions and relationships
• Deploying self-service BI tools that non-technical users can navigate confidently
• Aligning analytics staff with specific business domains for deeper expertise
• Adopting software engineering practices for data pipeline reliability

So far we’ve found the resulting reduction in ad hoc requests frees up more time to focus on platform improvements, which creates a virtuous cycle of increasing productivity.

What FreeAgent Analytics Engineers Do

Unlike traditional BI Analysts who primarily build reports, our Analytics Engineers:

• Design and maintain modular, tested data transformation pipelines
• Develop reusable components that business users can assemble into custom insights
• Implement version control, code review, and CI/CD for data assets
• Work alongside our data platform engineers to optimize data architecture
• Serve as domain specialists understanding technical requirements and business context

We also need to provide expert help and support in addition to the platform work on occasion. The domain alignment allows analytics engineers to have the depth of knowledge and stakeholder relationships they need to also provide support with those trickier analysis problems when and where it’s needed.

Summary

Analytics Engineering reflects a fundamental shift in how data serves our business. This means spending more time enabling others and less time carrying out detailed one off analysis projects. Rather than gatekeepers of information, our analytics team have become enablers—building systems and tools that democratize data access while maintaining quality and consistency.

For FreeAgent, the analytics engineering approach has delivered more reliable data, faster insights, and greater business agility—proving that how you structure your data team is just as important as the tools they use.