WHO IS A DATA ENGINEER? Skills Requirement And 2023 Salary


In the past, data was only viewed traditionally. But over time, its scope became broad bringing about data-related fields. These fields began to take some shifts which brought about a focus on data management. Management required some skilled set of people who could lay down the foundation of a database giving rise to the role of data engineers in our world today. A data engineer is responsible for the development and maintenance of the database. They make use of database techniques and implementations to build creative architectures. They also carry out tests at regular intervals to ensure the smooth running of programs without any problems.

Data engineers also support data scientists in improving data quality.

What is a Data Engineer?

A data engineer is someone who arranges specified data from vast resources, through the practice of designing and building systems that collect, store, and analyze these data at a specific ratio.

What is Data Engineer Skills?

These are the necessary skills one needs to have to become a data engineer. They are important skills as they help create a better understanding of a data engineering career.

Skills Required To Become A Data Engineer

#1. Coding

Coding is the means through which humans communicate with computers. It is a set of instructions that tells the computers what to do, how to behave, and what actions it has to take. As a data engineer, you must have good coding skills to work seamlessly with various programming languages related to data science. There are numerous programming languages, but the most common are Java, Python, and R. Now, these languages not only help to understand how to use proper database systems but also to use distributed systems efficiently.

#2. Communication skills

As a data engineer, you need to be able to communicate your ideas, suggestions, and resources effectively with colleagues in the project. Though data experts such as data scientists and architects often work hand in hand with the data engineer, you also need to communicate your ideas with other colleagues without any technical knowledge.

#3. Knowledge of operating systems

Data engineers are often responsible for selecting the right operating system (OS) to manage other application programs. One can only know the right operating system to use if given the proper knowledge. Therefore, having full knowledge of operating systems like Apple macOS and Microsoft Windows is very important for any who wants to be a data engineer.

#4. Data analysis

A data engineer needs to apply systematical and logical statistics when having to evaluate data. Having an understanding of analytic software like Hbase helps provide solutions to important tasks ahead after the required data are collected.

#5. Basic understanding of machine language

The study of machine languages is the main focus of the data scientist, though the data engineer also works hand in hand with the scientist. It is necessary to have at least, the basic knowledge of machine languages to work effectively with the data scientist on a project. In addition, it also serves as a plus to have such knowledge because it makes you a valuable asset to the company or project

#6. Critical thinking skills

The ability to carefully examine issues and create solutions that are not only effective but productive is always needed for most projects. Having this advantage as a data engineer is of great value in that most of the time you are going to have to create solutions that do not exist yet. Making the ability to think creatively key importance.

#7. Data warehousing

Data warehouses store a large amount of both past and recent data for regular analysis. These data are gotten from different sources and used by the company for checking out reports. Every data engineer needs to be familiar with the ecosystem of data storage and warehousing, and how to operate the tools.

#8. Presentation skills

Data engineers are sometimes expected to present their research to prominent stakeholders of the company after carrying out one’s analysis. Knowing how to be compelling, by explaining how your technical data help solves a company’s problem effectively increases the chances of actions on their recommendations.

#9. Database systems

Database management systems (DBMS) is a software application that is used to provide a database interface for the storage and retrieval of sensitive information in an organization. As a data engineer, you must know how to manipulate this system to achieve the required standard needed by an organization you are to work for. To do this, you need to know about database systems such as SQL and NoSQL.

#10. Data APIs

APIs are interfaces that enable software applications to access data and communicate with each other for a given task. A good example is the APIs in web applications. In web applications, API allows the front-end functionality to interact with the back-end data. These APIs are built into databases by data engineers to enable both data scientists and intelligence analysts to convey queries on the data in these systems.

#11. Automation and Scripting 

Data engineers are often having to deal with huge amounts of data. Since huge data is crucial to the data scientist and AI team, knowing how to deal with such data is vital. To optimize your work and get the best value, one has to be able to write specific scripts to automate repetitive tasks to reduce the time spent. Therefore, knowing how to write scripts and automate your work is a very important skill for any data engineer.

#12. Time Management

Being a data engineer, you are responsible for a lot of work. Each activity has to be done within the time given to it. Knowing the right time for each activity makes it easier to work comfortably with no pressure. At the same time, reduces the rate of error and efficiency in the organization.

#13. Amazon Web Services (AWS)

Amazon web services (AWS) is a cloud program that helps programmers become more creative and professional in their work. To data engineers, knowing the formation and designing of a cloud-based framework enable them to use amazon web services (AWS) to design complete automated data flows. This makes them stand out in an organization.

#14. Data security and governance

Although, data engineers are not necessarily responsible for data governance. But they also have to ensure that systems are in the right place for easy data access and user control. Making the knowledge of governance is also important to place better support for data governance concepts.

#15. Adaptability 

In general, adaptability is a highly recommended skill for any field. One has to work effectively with the change in the surrounding environment at any given point in time to get desired results. Data engineers are also not absent, so it is required by the organization.

#16. Strong Work Ethnic

Organizations don’t want to see employees who come to work doing only minimum. They expect their team to hold themselves accountable as part of the company’s success, requiring them to work to the best of their abilities in building the organization. Data engineers need to work at their best to help build the company’s success and this can only be possible with one having a strong work ethic.

#17. Real-Time Data Processing 

Another important data engineering skill is the knowledge of real-time data processing tools for data streaming. Having to process huge amounts of data, there is a high task complexity. But with real-time, data are developed and processed faster in event streams. Some examples of these real-time platforms are Kafka and Spark.

#18. Big Data tools

Big Data are technologies used to handle huge amounts of data available in different forms. To retrieve a piece of information from a big set of data, data engineers need to know the tools that can deal with big data. An example of such a tool is Apache Hadoop.

Apache Hadoop is a tool that works as an all-in-one solution in helping data engineers solve problems associated with big data. It is made up of multiple tools, that use clusters of machines to allow a large amount of data in sets to be processed while sitting as a single unit. 

With knowledge of Hadoop, one can easily create large-scale data processing applications that can be used for extracting data.

What is the Salary of a Data Engineer In 2023?

Data engineers are well-paid employees in any organization due to their expertise in their work. A data engineer makes an average annual salary of about $124,210 in the US. Meanwhile, data engineers can also get an average additional compensation of $26,370 on their salary. But the total compensation the data engineer can get on his salary is $150,581.

What Does It Take To Become a Data Engineer?

You may start or advance a successful career in data engineering with the correct mix of skills and knowledge. A bachelor’s degree in computer science or a similar subject is common among data engineers. By completing a degree, you may lay the groundwork for the information you’ll need in this rapidly changing sector. Consider pursuing a master’s degree to advance your career and gain access to possibly higher-paying positions.

Aside from obtaining a degree, there are a number of other steps you can take to position yourself for success.

#1. Improve your data engineering skills.

As a starting point for a career in data science, learn the principles of cloud computing, coding skills, and database architecture.

  • Coding language proficiency is required for this profession, so consider attending courses to gain and practice your skills. SQL, NoSQL, Python, Java, R, and Scala are examples of popular programming languages.
  • Databases, both relational and non-relational, are among the most used data storage methods. You should be knowledgeable about relational and non-relational databases, as well as how they function.
  • ETL systems (extract, transform, and load): ETL is the process of moving data from databases and other sources into a single repository, such as a data warehouse. ETL tools that are commonly used include Xplenty, Stitch, Alooma, and Talend.
  • Data storage: Not all types of data, especially massive data, should be kept in the same way. You’ll want to know when to employ a data lake vs a data warehouse, for example, as you create data solutions for a corporation.
  • Automation and scripting: Because organizations may collect so much data, automation is a vital aspect of working with big data. To automate repeated processes, you need to be able to develop scripts.
  • Machine learning: While data scientists are more concerned with machine learning, understanding the fundamental ideas will help you better understand the demands of data scientists on your team.
Read Also: Machine Learning: All You Need to Know about Machine Learning
  • Big data tools: Data engineers do not only work with traditional data. They are frequently responsible for managing large amounts of data. Hadoop, MongoDB, and Kafka are some popular tools and technologies that are changing and differ per firm.
  • Cloud computing: As firms increasingly trade physical servers for cloud services, you’ll need to comprehend cloud storage and cloud computing. Beginners should look into Amazon Web Services (AWS) or Google Cloud.

While some businesses may have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to prevent loss or theft.

#2. Obtain certification.

A certification can certify your skills to future employers, and studying for a certification exam is a great opportunity to expand your knowledge and skills. Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, and Google Cloud Certified Professional Data Engineer are all options.

Examine various job postings to see what positions you could be interested in applying for. If you notice that a specific certification is regularly listed as required or recommended, it is a fantastic place to start.

#3. Create a data engineering project portfolio.

A portfolio is frequently used in job searches to demonstrate your abilities to recruiters, hiring managers, and future employers.

A portfolio website (created using a service like Wix or Squarespace) can be used to upload data engineer tasks that you’ve accomplished independently or as part of the class. Alternatively, you can upload your work to the Projects section of your LinkedIn profile or to a site like GitHub, both of which are free alternatives to a standalone portfolio site.

Improve your big data skills with a portfolio-ready Guided Project that takes less than two hours to complete. Here are some options to get you started with no program downloads:

  • Make Your First NoSQL Database Using MongoDB and Compass Database Design with SQL Server Management Studio (SSMS)
  • MYSQL Workbench is used to create and model databases.

#4. Begin with a low-level position.

Many data engineers begin their careers in entry-level positions such as business intelligence analysts or database administrators. As you gain experience, you will be able to learn new skills and qualify for more advanced positions.

What are the Data Engineer’s Responsibilities?

A data engineer is responsible for :

  • Collecting, managing, and converting raw data into information.
  • Interpretation and evaluation of business objectives.
  • Analyzing data and creating reports on the given results.
  • Building algorithms and required prototypes.
  • Development of tools and programs for proper analysis.
  • l Communicating effectively with data scientists for better results on working projects.

Do Data Engineers Do Coding?

Yes, data engineers do coding. You need to have proper knowledge of coding programming languages to become a data engineer.

Is Data Engineering a Good Career?

The world is growing so fast with so much information that has been processed from raw data. So far as this data is always available to be processed, data engineers will always be in high demand. Choosing data engineering would be a good career for anyone in the future. 


Data engineering is a very important career, and the increase in cloud-based services continues to create more demands for data engineers. You don’t necessarily need to be an expert in all skills, simply get the basic knowledge of these skills and focus your experience on solving real-life problems that showcase your talents to the world.





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