The role of the data engineer

If you belong to the X or previous generations, you did not grow up in the big data era. Roles such as data scientists or data engineers did not exist at the time you studied or were reserved for a minority of misunderstood geeks. Things have changed dramatically. Today, the data business is unavoidable. For Millennials and Gen Z, making decisions based on data flows is becoming the norm. In this article, we present the essential role of data engineers in business.


The data engineer, the architect of data flows

There is nothing more frustrating for a data engineer than being confused with a data scientist and vice versa. If these two functions could once overlap, the complexity of the systems and the increase in data made the distinction necessary.

The data engineer is the essential link to correctly select, sort, and organize data flows from different sources. The data engineer orchestrates databases and must always keep in mind two fundamental aspects: system scalability and data security. Without this, the data scientist will not be able to analyze the data properly and draw useful conclusions for the company.

If data is compared to a large library, data engineers are responsible for selecting and organizing books so that readers can navigate effectively. Some will describe this task as ungrateful. It is true that a good data engineer must be able to listen to data scientists in order to provide the information they need. Data engineers often work in the shadows, but their role remains crucial because they define the architecture. Without solid foundations, a building is doomed to collapse. This can be compared to a back-office function. However, the data engineer function can also be exciting. It all depends on the organization for which the data engineer works and the autonomy given to this role.

Data engineers

A good data engineer has a variety of technical skills. It must know the various tools available to propose the best solution to its counterparts. Here are the main technical skills considered necessary:

  • good knowledge of the various distributed data processing solutions on a large scale;
  • good mastering of structured computer languages, java and python to begin with;
  • good knowledge of database management systems;
  • good mastering of conventional operating systems.

These skills will help data engineers determine the best architecture for your data. However, data engineers now have many external tools at their disposal. It is therefore no longer necessary to always know how to code on a large scale.

The data engineer must of course keep up to date with new developments and updates in his field. The search for simplicity and rationalization are essential to avoid over-complexity of companies.

In terms of soft skills, a good data engineer must be able to communicate correctly and be able to adapt to different situations. Too many data engineers simply reproduce the same logic to the different problems that arise without seeking to innovate or rethink their architecture.

Communication and cross-cutting, two differentiators for a data engineer

The data engineer is at the service of the organization. It must be able to have an overview of the data sources and the needs of the company. The best data engineers have an excellent analytical mind to understand the different elements that need to be considered in developing the most efficient data architecture.

Beyond these analytical skills, the data engineer must be team minded. He must be able to communicate with the various players in the company to best meet their needs. Data engineer customers are internal to the company and it is crucial to understand their needs to hope to meet their expectations correctly. This need is sometimes misunderstood by some aspiring data engineers who imagine themselves coding in isolation as they see fit. This type of profile should be avoided when recruiting a data engineer.


Security and scalability, the importance of an analytical mind

The data engineer must always keep in mind two elements in all aspects of the work: safety and scalability. Data must be protected and secured at all stages of processing. They must also be authenticated and unalterable.

Decisions made on compromised data can have disastrous consequences. Imagine that a doctor determines your treatment based on an incorrect or incomplete medical record and you will have an idea of the damage that a data breach can cause.

Second, the scalability of the systems is essential. Data flows are constantly increasing, and a system needs to be designed from the outset to process large volumes of data.

Data engineers unfairly denigrated by data scientists?

The prestige of data engineers varies greatly from one organization to another or even from one country to another. In fact, data engineers are essential to data scientists and vice versa. In fact, the wage differentials are often smaller and the balance does not necessarily lean on one or the other position.

Erasing the rivalry between the two functions is an essential element in achieving harmonious collaboration and therefore more efficient data processing. Make no mistake, it is the data that makes the law in business and not those who process it. Like a river that joins the ocean, this is the life of organizations. Data engineers and data scientists are only there to channel and make the most of them along the way by building dams, using hydraulic energy, or feeding on the fish in them.

Changing role of data engineer

A good data engineer is an essential role in a data-driven company. Nevertheless, the function has changed considerably in a few years. Today, many software available on the market automate some of the work of data engineers. They no longer must organize databases from scratch, but can use existing tools to create a flexible, scalable and dynamic data architecture to best meet the needs of the business.

A good data engineer in 2020 is above all pragmatic and knows how to take advantage of existing technology. For small or medium-sized organizations, on-demand software or SaaS are often significantly more efficient than home-made solutions.

Ryax helps companies industrialize their data through its data engineering platform. This valuable and scalable tool helps your teams deploy, execute, and scale their production models. Do not hesitate to get in touch for more information about this product and the implications for your business.

The Ryax Team.