The Analytics Engineer

Data warehouse automation is a way to develop your data warehouse faster and more efficiently. By automating manual operations, working on your data warehouse becomes much less complex. This makes it possible to bring multiple steps in the data process together. As a result, you see the traditional roles in a data team changing. With this new way of working, it is no longer necessary to put together a team of various specialists. This creates a new role: the Analytics Engineer. To illustrate this, I compare the situation without and with data warehouse automation below.

The traditional way of working

When you develop a data warehouse the traditional way (without automation), many steps and specialists are needed to turn raw data into valuable insights. Specialists such as database administrators, data engineers, front-end developers and business analysts are all involved in this process. Because so many specialists are involved, the risk of errors caused by miscommunication or noise is greater. This in turn often leads to delays.

The new way of working with data warehouse automation

Low-code data warehouse automation tools, such as TimeXtender, mean fewer specialists are needed to build data solutions. This makes the process from raw data to insights much faster. The Analytics Engineer can carry out the steps that were previously performed by various specialists. This makes your organization less dependent on staff who are often hard to replace.

The profile of the Analytics Engineer

Thanks to the technology that low-code tools offer, in-depth technical knowledge is no longer needed to develop a data warehouse. Although working with low-code automation platforms still requires knowledge of data and modeling, this is easy to train. A reasonably tech-savvy person with a good dose of Excel knowledge can learn it. This means the same person who now builds dashboards can soon also build the data structures in the data warehouse.

Below you can see the difference between the traditional way of working and the new way of working with an Analytics Engineer:

The benefits of the Analytics Engineer

• Make use of your own talent: Analytics Engineers can be trained internally. Technically skilled employees with domain knowledge are very well suited for this.
• Fast learning curve: Thanks to intuitive low-code software such as TimeXtender, various roles can quickly grow into an Analytics Engineer.
• Shorter lines of communication: The Analytics Engineer manages the entire (data) chain, from question to report.
• Talent availability: Broad skills can be learned faster. This makes it easier to find and train suitable talent for data warehouse automation.

Working with an Analytics Engineer

At E-mergo we see that working with an Analytics Engineer works well. We strongly believe in making our customers self-sufficient with the help of low-code tools. For many SMEs, hiring multiple specialists is often not feasible. With a number of Analytics Engineers, a company can still work effectively without depending on one particular specialist. This means there is no longer a single point of failure. Moreover, Analytics Engineers are closer to the business, so they often understand organizational issues faster. This makes it easier to work with other stakeholders in the organization.

Want to know more about this approach? Get in touch with our colleagues. We are happy to share our experiences.