Data Science

Career Guide: How to Become a Data Engineer

Written by Karin Kelley
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Updated on January 18, 2024

Look at any job website, and you’ll find many posts for data engineers. After all, data is the cornerstone of modern business. But what does a data engineer do, exactly? Also, if you’re wondering how to become a data engineer, read on. In this blog, we’ll cover what these essential professionals do and share tips on pursuing this career path, including an online data science program you can take to get you on your way.

What Does a Data Engineer Do?

The job of a data engineer is a highly technical position in which the individual is an expert in database technologies, programming languages, and distributed systems. They create and maintain data pipelines and warehouses that ingest, process, and store data. This data can then be accessed and analyzed by data scientists or analysts. Their job is to transform these complex data sets into insightful and valuable information that can be leveraged by those without technical skill sets. Data engineers, for example, may transfer this information into a comprehensive visual report that enables stakeholders to make informed business decisions.

Also Read: Top Data Science Projects With Source Code to Try

Why Pursue a Career in Data Engineering?

Data engineering is a rapidly growing field that is expected to increase in demand by 21 percent between 2018 and 2038. Although the profession requires a technical skill set, it is highly rewarding, and data engineers play a crucial role in the success of organizations of all sizes. Because of this, there are many job opportunities with competitive salaries. Likewise, data engineers are often supplied with the latest cutting-edge technologies to conduct their work. For those interested in a rapidly changing work environment that involves intricate data problems with real-world outcomes, a career in data engineering presents an immense opportunity for growth, development, and work/life balance.

Now, let’s dig into how to become a data engineer.

The Data Engineer Learning Path and Steps to Become a Data Engineer

To be successful as a data engineer, there are several complex skills that one must first develop. These skills are usually learned by taking various data science, engineering, coding, computer sciences, or software engineering courses. Not all data engineers have college degrees; however, most have a bachelor’s or master’s degree in computer science or computer engineering. To remain competitive, someone not currently established in the field should seek a relevant four-year degree from an accredited university.

Those with a college degree can switch from their current field to data engineering by taking various certification courses and gaining practical experience. To start with the basics, learn the foundations of computer science and programming languages. Although this is likely the most time-consuming step, it is essential as the remainder of the skills that need to be developed all lean on these basics. To learn how to become a data engineer, follow this learning path:

Learn Python, Java, or Scala Programming Languages

Learning the most popular programming languages is the first step in becoming a data engineer. They are used in all aspects of the job and are critical for tasks like data extraction, transformation, and loading (or ETL). Programming languages are also needed to create and maintain data pipelines, which is an essential responsibility of a data engineer.

Dive into Big Data and Develop Your Knowledge of Cloud Technologies

To gain employment as a data engineer, one of the major requirements is to know how to use the tools of the trade. The industry relies on popular systems, including Hadoop, Spark, and Kafka. It can be challenging to learn to use these technologies without hands-on experience on the job; however, there is no shortage of tutorials, classes, and certification options. Start with the basics and slowly move on to more complex matters after the basics have been mastered.

Get Familiar With Databases

Creating, building, and maintaining databases is an essential duty of a data engineer. Once the basics of programming and big data technologies have been mastered, familiarize yourself with databases. Databases like MySQL, Oracle, and PostgreSQL are good places to start. As with the previously mentioned technologies, communities, online data science courses, tutorials, and open-code source database projects are available for those who wish to learn how to utilize these databases for data engineering.

Also Read: The Ultimate Guide to Statistics Interview Questions for Data Scientists

Obtain Hands-On Experience With an Entry-Level Position

Data engineering is a complex position that merges many concepts into one role. From data modeling and programming languages to building data pipelines, those in the industry typically are not beginners. Gaining hands-on experience is best done on the job, and an entry-level position in data science, data engineering, or computer sciences is a great starting place. All the basic skills needed can be refined in this position, and additional valuable ones can be picked up along the way. For instance, a person can learn about stream processing and ETL frameworks and start to build a portfolio of data engineering projects to which they have contributed. Having related experience to add to a resume is also extremely valuable for securing an initial position as a data engineer.

Apply for Your First Job as a Data Engineer

Once you are confident in your abilities, you can apply for your first job as a data engineer. During this phase, it is best to look for entry-level positions or those that offer a lot of additional support. This will ensure that you have access to guidance if needed and can still exercise your newly learned skills and gain confidence on the job. Likewise, it is best to aim for a “generalist” position at the beginning of your career. This will allow you to remain well-rounded and continue to hone skills that will be useful throughout your career. A specialist position, on the other hand, may restrict you to specific functions and tasks and may limit your development.

Prepare for the Data Engineering Interview

As with interviewing for any position, brush up on your basic interview skills. This includes communication, eye contact, and preparing for interviewers’ questions during the initial meeting. For instance, the interviewer may ask questions seemingly unrelated to data engineering, such as, “Tell me about a time you failed at a task and persisted?” This is irrelevant to the job, but your response will indicate how you will handle difficult situations. This is essential in data engineering as things often go wrong, and troubleshooting is needed. Prepare answers to common interview questions in advance and practice speaking them out loud so that they come naturally during interviews.

From a data engineering-specific standpoint, brush up on the fundamentals. For instance, an interviewer may ask you to explain what data engineering is. You likely know this answer, but without a prepared response, you can easily leave out vital components that the interviewer seeks in your response. It is also helpful to present a prepared portfolio of data engineering projects to interviewers, along with researching the company you are interviewing with before the interview takes place.

How Much Does a Data Engineer Make?

Now that you know how to become a data engineer, let’s discuss potential salaries. The salary for a data engineer varies widely based on their experience, location, the size of the company, and any additional certifications they may have. On average, salaries range between $75,000 for entry-level positions and a maximum of around $160,000 for highly experienced, senior positions. A data engineer in the middle of their career can expect to earn an average of $116,000 annually in the United States.

Who Employs Data Engineers?

Nearly any organization with large amounts of data will employ a data engineer to enable them to collect and later analyze data. Most commonly, these organizations are in industries like technology, healthcare, e-commerce, or financial sectors. While it typically isn’t necessary for a data engineer to have experience in the industry in which they are employed, it can be helpful. This is particularly true if the data engineer has prior experience working with highly sensitive and confidential data such as healthcare or financial data. Because specific rules and regulations are in place for these industries, this can give data engineers a competitive edge in the job market.

Also Read: A Guide to PySpark Interview Questions for Data Engineers

What’s the Difference Between a Data Engineer and a Big Data Engineer?

While many aim to become data engineers, others want to learn precisely how to become a big data engineer. The two are essentially the same, and the duties and responsibilities are similar. Big data engineers work with much larger data sets than traditional data engineers. They work with enterprise-level organizations and are often also responsible for aiding with scalability as the datasets continue to grow.

How to Become a Data Engineer — Take an Online Bootcamp!

Developing all the skills needed to become a data engineer can be time-consuming and lengthy. Despite this, the time it takes to gain these skills largely depends on the level at which the individual is starting. Someone already employed in a similar field may know the basics of programming languages and databases. In contrast, someone who does not know these foundations will take much longer to gain employment as a data engineer.

In either case, anyone interested in this career path can develop the skills needed through this data science bootcamp. The course can be completed in as little as six months and covers all the necessary skills, from data visualization to statistics and data analysis.