In the past year I have gained experience in a relatively new field that sits between Data Analytics (DA) and Data Engineering (DE). The term “analytics engineering” is gaining more and more attention and, in my opinion, is set to become an important branch of the analytics field. But what exactly is analytics engineering, and why is it important? In this blog post, we’ll dive into the world of analytics engineering, exploring its definition, importance, and applications.

What is Analytics Engineering?

Analytics engineering can be defined as the process of designing, building, and maintaining data pipelines that support data analytics and machine learning. These pipelines ensure that data is transformed into a usable format that can be used for various applications, such as reporting, dashboards, and predictive modeling.

To put it simply, analytics engineering involves the creation of the infrastructure needed to support data analytics. This includes tasks such as data modeling, data integration, data transformation, and data quality assurance.

Why is Analytics Engineering Important?

The advent of low-cost cloud storage and the extensive opportunity to scale the infrastructure is leading to a paradigm shift. We are now moving towards more ELT frameworks (Extract, Load and Transform), where the data can be transformed through SQL directly within the database. This Transformation layer is where the role of an Analytics Engineer comes into play.

There are several reasons why analytics engineering is important:


As data volumes continue to grow exponentially, it’s becoming increasingly challenging for organizations to manage and analyze their data effectively. Analytics engineering enables organizations to scale their data analytics capabilities, ensuring that they can handle large amounts of data and derive insights from it.


Analytics engineering can help organizations streamline their data processing and analysis workflows, improving efficiency and reducing costs. By automating tasks such as data integration and transformation, analytics engineering can reduce the time and resources required to produce actionable insights.


Analytics engineering is critical for ensuring data accuracy and quality. By establishing robust data pipelines and implementing rigorous data quality checks, organizations can ensure that their data is reliable and trustworthy.


Analytics engineering enables organizations to be more flexible in how they use their data. By creating data pipelines that can handle various types of data, organizations can easily adapt to changing data needs and use their data in new and innovative ways.


The applications of analytics engineering are cross-functional, and for this reason, I expect to see an increase in demand for these types of roles across multiple industries as more and more companies try to harness the power of their data.


I’ve been positively surprised by this field. I enjoyed working within this novel field, and I see a lot of benefits that can come from applying these methodologies to classical ETL/ELT pipelines.

If you want to learn more about analytics engineering, please reach out!”