04 So, how to become a data engineer?
As you may be well aware, data engineering is one of the hottest trends in tech right now (you can read more about current trends in our article “What are the biggest tech trends in 2023?”). So probably many people are wondering now what they can do to become one. This article will take a closer look at what data engineers actually do and how to become a valued data engineer.
What is Data Engineering?
It’s pretty easy to explain what data engineers do in a nutshell - they design and build systems that will collect, store and analyze data at a large scale. This type of position is vital in organizations across all industries. The larger the company, the more data it collects, and the need to have engineers that will make the data usable and accessible for analysis is essential. This allows data scientists and analysts to do their job properly.
This type of job also gives you the feeling of doing something truly meaningful as the amount of data collected every day is unimaginably big - and I think at this point everyone understands that properly analyzed data is one of the biggest assets for most organizations and our lives as well (especially if you take a closer look at the usage of big data in the medical industry).
Check out our detailed article What is data engineering? Complex guide with examples
What does a data engineer actually do?
As mentioned above, their job is to build systems that collect, manage and convert raw data into useful information for data scientists and business analysts. Thanks to that, the organization can analyze and optimize processes related to the data gathered.
Among the tasks in the job description, we can find things like
- Defining datasets and aligning them to fit business needs
- Planning and creating database pipeline architecture
- Creating and managing data validation methods
- Developing and maintaining algorithms that take care of transforming raw data into useful information
- Making sure to follow the compliance with data governance rules and internal security policies
- Be an active member of business meetings to understand company business objectives to be able to align their everyday work to them
These are just some of the tasks, but I tried to pick the most important ones. Naturally, the larger the organization, the smaller the number of tasks that will be a part of the job. It’s because of higher job specialization, but also because the amount of data is much bigger than in smaller companies.
Is it a good idea to pursue this type of career?
Now that we know the gist of the job let’s try to answer the question if this career path is worth pursuing.
In my opinion - definitely yes. This job can be very challenging but rewarding at the same time. It gives each person involved in the process the feeling of having a genuine impact on business. You will become a vital part of any organization and will be able to use problem-solving and engineering skills to work on scalable solutions.
It’s one of the jobs that are in huge demand, so it shouldn’t be hard to find a place for you to grow. And speaking of growth - starting with a data engineering position, you can easily move into other roles like management, data architect, solutions architect, or machine-learning-related positions.
So, how to become a data engineer?
Now let’s take a look at steps you can take to become part of the data engineering world.
Data Engineering Education
First of all, you should consider a data engineering education. Usually, any education related to data science, software engineering, math or business-related field should be good enough.
You should look for studies related to data engineering (look for systems architecture, database administration, and programming in the curriculum).
If you do not want to pursue university studies, you can also look for a longer course that will teach you the fundamentals needed to get an entry-level position. In my opinion, whatever you do, you have to bear in mind that the data engineering position is strictly related to business. Hence having some basic business-oriented knowledge is vital.
Data Engineer Skill Set
Even with the best education, you still need to be careful about the specific skill set required for this type of career. I’ll try to mention a few essential skills. Still, you need to remember that the data-engineering tech landscape is changing rapidly, so you always need to do additional research on necessary skills (sometimes it also changes depending on the organization you want to be a part of).
I think it’s pretty obvious that data engineers' daily work is heavily dependent on databases, so being extremely fluent in SQL and having experience with different databases (MySQL, PostgreSQL, SQL Server) is essential.
But SQL is not everything. You may also know that non relational databases (NoSQL) are part of big data and real-time applications. Of course (in my opinion), you do not have to be fluent in NoSQL, but at least know the concept, understand the basics, know when to use those, and be prepared to learn more anytime.
Similar to databases, programming is essential to the job. Naturally, the number of programming languages is overwhelming, but luckily there is a simple choice you cannot go wrong with - one and only Python.
It’s the most popular language used in data engineering and processing. It’s also heavily integrated with frameworks and tools related to data engineering, like Apache Spark or Apache Airflow. Another programming language that may be useful is Java or Scala, as these tools run on Java Virtual Machine (JVM).
- Distributed computing frameworks
This technology area is increasingly combining with data engineering. What is it? It’s simple - a distributed system is an environment in which various components are spread across multiple devices on a shared network. They are to split the workload to different computers to run them efficiently. Having some basic knowledge of frameworks like Apache Hadoop and Apache Spark (again) is pretty important here.
- Cloud technology
This is another piece of the puzzle that creates a full developer data engineering landscape. Currently, most business systems are connected to cloud-based systems like AWS, Azure, or GoogleCloud. As they are commonly used to manage some operations, a good data engineer should have at least a basic understanding of their pros and cons and usage in Big Data projects.
- Stream processing frameworks
At last, we’ve come to the most exciting part of data engineering - processing real-time data. This means that candidates are expected to be fluent with stream processing frameworks like Flink, Kafka Streams, or Spark Streaming. So if you want to distinguish yourself from the crowd of candidates, check those as well.
It’s obvious, it’s simple yet really important. Many jobs will involve writing shell scripts, so know your way around the terminal.
- Soft Skills & business skills
To finish the skills list with something different, I strongly recommend working on your soft skills, communication skills, and understanding of the business perspective. Your everyday job will involve communicating with business people and understanding their goals - that’s why in my opinion, soft skills are somewhat essential to do the job properly.
As you can see, the data engineering profession is fascinating and versatile. Hence the number of skills that need to be gathered is pretty extensive (and bear in mind that I merely skimmed through them, and the landscape is changing all the time).
But all the skills in the world will not guarantee success, so you should seek as much practical experience as possible to be an interesting candidate for your future employer. If you want to know more about the job, check out our careers page and contact us directly.
We will surely help you navigate those waters!
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