Top 8 Skills You Need To Become a Data Engineer (With Tips)

Updated March 23, 2023

One of the essential roles in any organization that handles large amounts of data is a data engineer. Data engineers are information technology specialists, and as such, they often have expertise in a variety of applications and processes. You can become an effective data engineer and a more eligible candidate by learning and developing these skills.

In this article, we explain the role of data engineers, explore some of the most important skills for this profession and list the steps necessary to start a career in data engineering.

Please note that none of the companies mentioned in this article are affiliated with Indeed.

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What do data engineers do?

Data engineers develop and maintain the architecture used in various data science projects. They're responsible for ensuring that the flow of data between servers and applications is uninterrupted.

Data engineering combines elements of software engineering and data science. Some of the primary functions of a data engineer include things like developing data collection processes, integrating new software and data management technologies into an existing system and streamlining the existing foundational processes for data collection and use.

Related: 10 of the Highest-Paying Engineering Jobs (Plus Job Outlook)

Essential data engineer skills

To perform their responsibilities efficiently and effectively, data engineers often possess the following technical and soft skills:

1. Coding

Coding is a highly valued skill that is a requirement for a majority of data engineering positions. Many employers want candidates to have at least a basic understanding of programming languages like:

  • Python

  • Golang

  • Ruby

  • Perl

  • Scala

  • Java

  • SAS

  • R

  • MatLab

  • C and C++

Related: 7 Best Programming Languages to Learn in 2023

2. Data warehousing

Data engineers are responsible for storing and analyzing an incredible amount of data. That's why familiarity and experience using data warehousing solutions, such as Panoply or Redshift, is imperative in a data engineering role. Due to the growing use of data warehouses, those with experience managing and analyzing data from them may be able to find more roles for which they have adequate qualifications.

3. Knowledge of operating systems

As a data engineer, possessing an intimate understanding of operating systems like Apple macOS, Microsoft Windows, Linux, Solaris and UNIX is vital. Different operating systems and devices provide various benefits and can meet unique needs, so understanding the nuances of each may influence your success within this field. Specifically, data engineers may use the Linux operating system to handle large amounts of data and unstructured data, while they may use Windows to manage server clusters.

Related: Types of Operating Systems (With OS Functions and Examples)

4. Database systems

Data engineers should have a deep understanding of database management. Since Structured Query Language (SQL) is considered the most widely used solution, gaining in-depth knowledge of it's extremely valuable in this field. SQL is a database coding language that extracts and manages data stored in tables. There are other database solutions, like Bigtable or Cassandra, to learn as well, especially if you plan on doing freelance data engineering.

Related: 10 SQL Skills for Programmers and Developers

5. Data analysis

Most employers expect data engineer candidates to have a strong understanding of analytics software, specifically Apache Hadoop-based solutions like MapReduce, Hive, Pig and HBase. A primary focus for engineers is to build systems that gather information for use by other analysts or scientists. Having strong analysis skills yourself can help you create such systems and improve them.

Related: 16 Common Hadoop Interview Questions (With Sample Answers)

6. Critical thinking skills

Data engineers evaluate issues and then develop solutions that are both creative and effective. Since there are often times when you might want to develop a solution that doesn't exist yet, the ability to think critically is key. Critical thinking also applies in the design and troubleshooting of data collection and management systems to find effective solutions to problems.

7. Basic understanding of machine learning

Though machine learning is primarily the focus of data scientists, it can be helpful for data engineers to have at least a basic understanding of using this type of data. Building your knowledge of data modeling and statistical analysis can help you create solutions that are usable by peers and set you apart as an incredible asset to any organization. Machine learning and its application to artificial intelligence is also a vastly expanding field across a broad range of industries, so learning about and understanding it can make data engineers more prepared to apply their skills to more career opportunities.

Related: How To Break Into Machine Learning in 11 Steps

8. Communication skills

As a data engineer, you collaborate with colleagues with and without technical expertise, which is why possessing great communication skills is important. Though you often work with other data experts, such as data scientists and data architects, you may share your findings and suggestions with peers without technical backgrounds. Strong digital communication skills, in both text and video and audio formats, are also increasingly important with remote work becoming more common in modern businesses.

Related: 48 Data Engineer Interview Questions (With Sample Answers)

How to become a data engineer

Follow these steps if you're hoping to become a data engineer:

1. Earn your bachelor's degree

Though there are many components that are equally as important as formal education when entering this profession, most employers require data engineers to have at least a bachelor's degree.

You may consider pursuing a degree in information technology, computer science, computer engineering, software engineering, applied math, statistics, physics or a related field. If you decide to pursue a degree outside of one of these majors, prioritize taking courses in coding, database management, algorithms or data structures.

2. Develop your skills

Internships are often a great way to grow your skill set and gain valuable experience, but you can also take on personal projects that allow you to grow your expertise in the field and develop your expertise with important solutions and programming languages, such as SQL and Python. Make sure that you incorporate these experiences into your portfolio so that you can show future employers what you're capable of.

Related: Best Online Data Engineering Courses (And How To Choose One)

3. Pursue certifications

Data engineering certifications are extremely valuable and are great for showcasing your abilities. Some of the top options include:

  • CCP Data Engineer from Cloudera: This certification specifically covers solutions from Cloudera. It's a great way to show employers that you have experience using ETL analytics and tools.

  • IBM Certified Data Science Professional: The IBM Certified Data Science Professional certification is a popular option in this industry. It focuses on developing skills related to big data applications.

  • Google Certified Professional: With this certification, you show employers that you're familiar with the basic principles of data engineering and can fill a position as either a professional or an associate in the field.

Related: 18 Top Data Science Certifications

4. Gain experience

Though finding an entry-level job in data engineering may be the option you prefer, any IT-related position can provide a great source of experience and give you exposure to handling issues in data organization. Aside from allowing you to develop your critical thinking and problem-solving skills, an entry-level job allows you to understand the different aspects of this industry, how it functions and just how collaborative it is. For example, data engineers work with data architectsdata scientists and management to collect, analyze and utilize data.

5. Consider a master's degree

Pursuing an advanced degree, while rarely a requirement, is a great way to grow your knowledge, develop your skills and advance your career. By earning a master's degree in computer science or computer engineering, you can become a more competitive data engineer candidate. You may also become more of an expert in a specific type of data analysis or machine learning, which can be a great way to demonstrate your value in the long run.

Related: What Is a Master's Degree in Software Engineering? (Plus Skills)

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