3 min read
The Analytics Engineer
Data Warehouse Automation is a way to develop your data warehouse faster and more efficiently. By automating manual tasks, it becomes much less complex to carry out work on your data warehouse. This makes it possible to bring multiple steps in the data process together. As a result, you see the traditional roles within a data team changing. With this new way of working, it's no longer necessary to put together a team of different specialists. This is how a new role emerges: the Analytics Engineer. To illustrate this, below I compare the situation without and with data warehouse automation.
The traditional approach
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 in this process, the chance of errors due to miscommunication or noise is greater. This often leads to delays.
The new approach with data warehouse automation
Low-code Data Warehouse Automation tools, such as TimeXtender, mean fewer specialists are needed to build data solutions. As a result, the process of turning raw data into insights goes much faster. The Analytics Engineer can carry out the steps that used to be performed by various specialists themselves. This makes your organization less dependent on staff who are often hard to replace.
The profile of the Analytics Engineer
Thanks to the technology low-code tools offer, deep 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 somewhat tech-savvy person with a good dose of Excel knowledge can learn it this way. This means the same person who currently builds dashboards can soon also build the data structures in the data warehouse.
Below you see the difference between the traditional approach and the new approach with an Analytics Engineer:
The benefits of the Analytics Engineer
- Making 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 develop into an Analytics Engineer.
- Shorter lines of communication: The Analytics Engineer manages the entire (data) chain, from question to reporting.
- 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 clients self-sufficient with the help of low-code tools. For many SME companies, hiring multiple specialists is often not feasible. With a few Analytics Engineers, a company can still work effectively without depending on one particular specialist. This means there's no longer a single point of failure. In addition, Analytics Engineers are also closer to the business, so they often understand organizational issues faster. This makes it easier to coordinate with other stakeholders in the organization.
Want to know more about this approach? Get in touch with our colleagues. We're happy to share our experiences.
Written by Stefan Timmerman
BI Consultant