How to Become a Data Engineer in 2024: Your Guide to This In-Demand Career Path

Companies around the world are relying on data to further their growth and serve their customers effectively. Large sets of data need to be organized and managed properly; otherwise, it’s difficult to handle and obtain the necessary data. That’s where data engineers step in! With data engineering skills ranging from knowledge in programming languages to building systems that manage big sets of data, data engineering is an in-demand career and is essential to many companies and corporations. If you’re interested, here’s a guide on how to become a data engineer in 2024.

Job positions for the data science ecosystem typically require a bachelor’s degree in technology-related disciplines. Since this field is relatively new, data engineering degrees such as a Bachelor of Data Science or a Bachelor of Data Analytics may not be available at your university, but you can pursue other related degrees such as computer science, information technology, and computer engineering.

These degrees introduce programming and concepts that speed up the process of learning skills related to data engineering and serve as an advantage in applying for jobs. However, if you have missed the opportunity to take them at university, no problem! There are online courses for data engineers that offer flexible schedules, and you can access them remotely. You could be a self-taught data engineer by building a skillset relevant to the job, which we will tackle in the next section.

While employers find formal education and course certificates valuable, they consider skills like computer programming, software engineering, and data fluency even more valuable. While those with a degree have the advantage of learning these skills during their time in university, those who completed a different degree or didn’t attend university can still learn and develop these skills through tutorials, hard work, and online resources.

1. Programming and Computer Language

Common programming languages in the world of data engineering are Python, SQL, Java, and Scala. Tutorials and guides are available online for those who wish to learn these languages, and activities are available for those who wish to challenge themselves and improve their skills.

For example, Interview Query has some exercises you can do to test yourself, such as this activity that challenges your analytical thinking and Python proficiency. Don’t worry if you get stuck because solutions are also provided! Dedicating time to practicing programming is essential on the road to becoming a data engineer.

2. Data Processing Frameworks

Data engineers often come face to face with Big Data (data sets that are too complex to be dealt with by traditional data-processing software). This is where Big Data Analytics and Framework come in! There are different frameworks available, such as Apache Hadoop, Apache Storm, and Samza. These frameworks allow for the distributed storage and processing of large datasets across clusters of computers using simple programming models.

It is important to understand that each project and case is unique, and the framework applied to one may not be the best framework for another. Proficiency in different frameworks and understanding how to use them greatly benefits the efficiency and success of your system.

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There are tutorials online to help you understand these different frameworks. This one by Simplilearn Solutions provides a tutorial for Hadoop, and this one from Tutorials Point talks about Apache Storm. There are also numerous videos on YouTube that you can check out!

3. Extract, Transform, and Load (ETL)

Another key skill data engineers need to develop is their proficiency in using extract, transform, and load (ETL) tools. ETL is the process of cleaning, organizing, and transforming data. Consider advancing your skills in using ETL tools such as Apache Airflow and Portable.io to improve your system and the accuracy of your data analysis.

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Courses dedicated to ETL are available online. Again, online learning gives you the flexibility to complete the course during your available hours and earn a certificate! Search for one that best fits your needs and enroll to help you prepare for the responsibilities that come with being a data engineer.

4. Build Yourself Through “Soft Skills”

Aside from learning languages and tools, certain characteristics are also needed for data engineers to be successful in their endeavors. Critical thinking and problem-solving are needed to figure out how to tackle a project, and organization aids in the arrangement of your code. You also need exceptional communication skills to effectively collaborate on projects with other engineers and scientists. Adaptability is a must, as there is no definite way to build a system, and the market is constantly changing. Programs and tools are only as good as the user, which requires self-improvement to advance in the field.

While learning these skills is vital for the role of a data engineer, you still need real-life experience, which employers value highly. So, let’s talk about various ways you can gain experience, like extracurriculars, certifications, internships, etc., to get yourself ahead of the pack (competition).

Join Extracurriculars

Organizations and clubs are spaces where a group of people can enjoy a common interest together. If you ask around, you may find one for programming and data science at your university!

Joining these organizations can boost your knowledge in the field, as well as allow you to meet people and make connections. These communities typically stay current on trends and news, and they can make sure you are aware of any opportunities that may arise. Some organizations even host events themselves, which is the perfect opportunity to expand your network and build your leadership and collaboration skills.

You can also attend functions that pique your interest. Just search online or ask around in your community for any events, whether online or local. You can go solo or group up with others and join competitions that challenge your knowledge in programming, enlist in data science case study contests to improve your critical thinking and problem-solving abilities, and attend conferences and seminars to learn from experts in the field.

Be open to these opportunities, as they not only help you improve yourself but may also help you stand out in the recruitment process.

Earn Certifications

Attending conferences and competitions typically earns you a certificate. There are numerous online classes and courses you can take to further enhance your skills, which also offer certificates upon completion. Companies such as Amazon Web Services and Google also offer exams that test your knowledge of their cloud services and provide certificates upon passing. These certificates prove your desire to learn, commitment to the field, and proficiency in various aspects.

Connect Your Hobbies and Interests with Your Career Path

Aside from joining organizations and collaborating with others, you may also build your portfolio by doing some personal projects. Maybe gaming is your hobby, and you decide to make a simple game for the fun of it. This may not be directly related to data engineering, but it showcases your creativity and proficiency in the programming language you used. You can create a blog post that demonstrates how you’ve studied and analyzed different topics, such as “How the rise of Airbnb accommodation in an area affected the average cost of hotels” or “Do people still go to the cinema, or do they just wait for the movie to be available on streaming platforms?”

This keeps your mind sharp and helps others understand the depth of your knowledge of relevant concepts and how you apply them practically.

Gain Hands-On Experience Through Internships

Internships are a traditional method of introducing someone to a job and providing hands-on experience in the field. Internship openings and listings can be found on websites such as LinkedIn, Glassdoor, and Indeed. You can also check the websites of companies you’re interested in working for to see if they have internship openings.

Considering how quickly the Big Data industry is growing, the competition becomes greater as well, which is why securing an internship may prove difficult. However, if you manage to land a position, you’ll be able to learn from experts and expand your network! You may also look at internships as practice for applying for a job since it’s typically the same process: apply for a position, attend an interview, and complete an assessment handled by the company.

You have the skills and some experience and are ready (and eager) to enter the workforce. Job hunting shouldn’t be taken lightly, as there are numerous job seekers eyeing the same position. While the job growth for data engineers seems promising, competition for these jobs is tough. Here, we will discuss the typical process of job hunting as a data engineer.

Send Your Application

First, you apply for a job. Companies usually have a “Career” tab on their website that shows their available positions. Similar to internships, job openings and listings can also be found on websites such as LinkedIn, Glassdoor, and Indeed.

Interview Query is a website that focuses on the data science ecosystem; jobs specific to it can also be found there. Search for jobs you are interested in and apply. Make sure to double-check and polish your resume to highlight your skills and showcase what you can contribute to the company!

Stand Out During the Interview

Once you’ve sent your application, the next step is usually an initial interview. You may need to do numerous interviews that focus on different things, such as 1) your background, 2) your technical knowledge, and 3) the responsibilities you would need to perform.

Here are some tips for preparing for a successful interview:

• Test your knowledge in the field.

You can test yourself and check your knowledge of different concepts in the field so you’ll be ready for any question.

• Study common interview questions.

Interview Query has compiled a list of common interview questions for technology-related jobs, and studying them beforehand allows you to fully prepare for the interview and answer the questions thoroughly.

• Participate in mock interviews.

Finally, having practice interviews helps build confidence in your speaking skills, so grab a friend who will pretend to be a hiring manager and have mock interviews, or connect with individuals who are also interested and knowledgeable in the field who will conduct mock interviews with you.

• Prepare for technical assessments.

Jobs that use programming languages typically have assessments that test your technical knowledge and skills. Assessments are generally divided into two categories: technical skills assessments and case studies.

Technical skills assessments are usually a timed, online test on your level of proficiency in programming languages or data tools and evaluate if your current level fits the job requirements. On the other hand, case studies reflect the real-world problems you would tackle once you are hired, and they allow the hiring team to test your analysis skills and how you approach problems.

Once you’ve submitted and completed your application—congratulations! Now comes the waiting game. In the meantime, you can stay up to date on data science, analyst, and engineering jobs by reading blogs. Keep yourself sharp by doing challenges and exercises related to data engineering. It may take a few days before you hear a response from the hiring team, and unfortunately, it’s possible you may not hear from them at all. Learning and acquiring the skills for the job is already tedious, but coupled with the competitive job market, you have a high mountain to climb.

While landing a job as a data engineer might not be easy, there are other jobs available for those with this skill set. Getting your foot in the door and working in an adjacent field can help you gain experience and knowledge of how data engineering works. Furthermore, ensure you’re adequately compensated by checking out the salaries for these jobs! As long as you have the drive and continuously build your skills and knowledge, becoming a data engineer is within your grasp!