Data Engineers: A Critical Part of Your Technology Team

The Dice Tech Job Report: Fastest Growing Tech Hubs, Roles and Skills found that the fastest-growing jobs in the technology sector were data engineers. For some, this may be startling — what about software developers? Or UX designers? And the many other high-demand tech jobs?

It’s true, there are many vital jobs in technology in today’s world — ones that play a critical role in our current and future landscape. But while it is, perhaps, not as well known as some of its counterparts, the role of data engineering is becoming increasingly relevant and important to numerous organizations.

What is a data engineer?

Data engineering, sometimes called big data engineering or information engineering, is the practice of developing systems that collect, store, scale, process, and enable the flow of information. Data engineers are responsible for devising plans forand building the architecture that other team members, particularly data scientists and analysts, process and evaluate.

A broad-reaching profession, data engineer responsibilities include developing, designing, sourcing, maintaining, monitoring, testing, managing, optimizing, and otherwise making it easier to leverage systems that handle data. They also ensure that the systems and tools they create make data applicable and useful in real-world scenarios. More specifically, data engineers create databases and algorithms that assist other team members to manage and handle information more easily.

There are more specialized types of data engineers, such as database-centric data engineers and machine-learning data engineers.

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Why do data engineers matter?

So, what does this all mean? Why are data engineers so critical to our modern businesses across a wide variety of sectors?

Consider digital transformation. It’s more than just a buzzword — it’s an entire way of thinking and being. When companies engage in a digital transformation, it signifies that they are prioritizing technology and adopting a digital-first mindset.

The new technologies of today’s world — the ones we embrace when we undertake digital transformation and acceleration — generate vast amounts of data. For those who do not have the skills and experience using huge amounts of unstructured (or even structured) data, this is overwhelming. But it’s also necessary to manage.

Consider, for example, healthcare organizations. Hospitals, insurance companies, and other organizations in the sector are responsible for collecting an enormous amount of patient information, information that is extremely sensitive. If it is not protected in a practical, secure way, then others could easily access it — to the detriment of both the patient and the organization. That’s just one reason why it’s critical to devise systems that facilitate the handling and management of data: security. And it’s one reason of many.

Data scientists make sense of data. They organize, track, and make this information digestible. But data engineers are an important part of the puzzle, too, because they make this data usable. Even for other data specialists, it is important to ensure that the information is presented in a coherent way — a way they can understand— and that’s why data engineers are critical in our world.

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What’s the difference between data engineers and data scientists?

How does the data engineer fit into the big picture? Many companies that deal with technology —which, let’s face it, is most of them, no matter what the industry — have entire teams dedicated to managing the overwhelming abundance of information they generate and manage. Along with the business leaders, these teams include roles like data analyst, IT specialist, systems architect, database manager, data scientist — and, of course, data engineer.

These roles all contribute to the management of a business’ data, although they have different skillsets and responsibilities. The data scientist and data engineer are often confused because of the complexity of their tasks. There is some overlap, but the jobs are distinct and complementary.

The rise of the data engineer actually came about because historically, data scientists were responsible for the entirety of data flow and process. At one time, they had to create the architecture necessary for transforming and facilitating the data flow and transfer. This was on top of their other core tasks, which, today, concern analyzing the data that is made presentable and usable by the data engineers. Now, once the data reaches the data scientist, it is in a usable state.

Data scientists also present their findings in a digestible format to lay and technical audiences. Meanwhile, data engineers operate behind the scenes, in some sense enabling their scientist counterparts to do their jobs optimally and efficiently.

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Top data engineering skills, background, and experience

Business leaders should also look for professionals with some experience, such as internships in data engineering or data science. Others may spend several years or longer as software engineers or in another technological role before moving into the data engineering space. While it’s not completely unheard of to enter this and other technological fields without, a bachelor’s degree in computer science or a related discipline is usually a must. Some professionals earn a master’s degree in this or another relevant discipline and/or pursue certifications as well, a testament to their abilities.

Data engineers need a combination of hard and soft skills in order to do their jobs effectively. Just some of the hard or technical skills you should look for in these professionals include:

• Experience with programming and scripting languages, including Python, C, C++, Golang,  SQL, NoSQL, Java, Scala, R, Perl, Ruby

• Experience with tools, libraries, and frameworks, such as ETL (Extract, Transfer, Load) tools, Apache Hadoop, Apache Airflow, Apache Spark, Apache Kafka, Amazon Web Services, Amazon Redshift, and Microsoft Azure

• Statistical analysis

• Statistical modeling

• Machine learning knowledge

• Knowledge of distributed systems

• Knowledge of operating systems

• Ability to create algorithms

• Knowledge of statistics

• Data warehousing

• Softwaremethodologies and mindsets like Agile and DevOps

• Data visualization

Meanwhile, relevant soft skills include:

• Collaboration

• Communication

• Presentation

• Time management

• Critical thinking

• Analytical thinking

• Problem-solving

skillsets

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The future of data engineering

As the abundance of data steadily grows and more and more organizations seek to undergo digital transformations, the data analytics space is becoming increasingly relevant and important. Thus, the roles of data engineers will become all the more paramount as well.

As the field becomes more well-known and prominent, we will likely see more niches and specialties within the profession. Additionally, newer, more sophisticated tools will aid data engineers in doing their jobs. Data will also become commoditized to a greater degree, as its importance is increasingly underscored.

For data professionals and businesses alike, this means an exciting future ahead — one that means greater insight into processes and organizational opportunities. At Nearsure, we can connect your business with top data engineers and other talented tech professionals to help usher your business into a new era.

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