What Does a Data Engineer Do? Check Out the Roles & Responsibilities!

Nowadays, the data engineering profession is one of the specializations rapidly gaining popularity in the Big Data ecosystem. According to LinkedIn’s 2020 Emerging Jobs Report, data engineering is among the 15 most outstanding emerging occupations of the last five years, sharing prominence with positions as suggestive as a data scientist, Artificial Intelligence, or Site Reliability Engineer expert. Data engineers are in the eighth position in this list, with LinkedIn confirming that their hiring has grown significantly by 35% between 2015 and 2019. 

And you, would you like to work as a data engineer? Is this a career path you would like to explore? Stay with us if you want to see what Big Data professionals are doing and if it fits your personality and goals.

What Is a Data Engineer?

An organization’s data engineer is responsible for laying the foundations for acquisition, storage, transformation, and data management. The specialist is responsible for configuring the necessary technological infrastructure so that a large volume of unstructured data collected becomes accessible raw material for other Big Data specialists, such as data scientists and data analysis. 

Data engineer’s work involves designing, creating, and maintaining the database architecture and processing systems so that the subsequent work of exploitation, analysis, and interpretation of information can be performed without incident, in an uninterrupted, effective and secure manner. 

What Does a Data Engineer Do Day to Day?

The data engineer’s day-to-day runs, usually between ETL (Extraction, Transform, Load) processes, including developing data extraction, transformation, and loading tasks. In addition, they are responsible for moving them between different environments and purging them so that it appears normalized and structured in the hands of analysts and data scientists. In such a way, the data engineer’s role is comparable to that of a plumber since it focuses on implementing and maintaining the network of pipelines in good condition through which the data (like water) will feed the system, improving the functioning of the whole organization.

  1. Extraction

During the initial stage of the ETL process, the data engineer is responsible for extracting the information from different locations and studying the incorporation of new sources into the company’s Big Data flow. This data is then presented in different formats, integrating very diverse variables, and sent to a data lake or another repository where this information will be stored raw and made available for further use. 

  1. Transformation

In the next step, the data engineer coordinates the data cleansing, eliminates duplicates, fixes errors, and discards unusable material. Moreover, they also elaborate and classify them to convert them into a homogeneous set. 

  1. Load

Lastly, the data engineer is also responsible for loading the data to its destination, whether it is a database located on a company’s server or a data warehouse in the cloud. In addition to the correct export, one of the frequent concerns in the final stage is security surveillance since the data engineer needs to guarantee that the information is kept safe from cyberattacks and unauthorized access. 

What Does It Take to Work as a Data Engineer?

Working as a data engineer demands acquiring the technical skill required for the complete ETL process. Most businesses ask their candidates to know how to use SQL and NoSQL databases, to be familiar with cloud services (such as Amazon Web Services or Microsoft Azure), and to move freely within the Hadoop ecosystem (Hive, MapReduce, etc.). 

It will also help if the data engineer knows of: 

  • Apache Spark: An open-source software that is among the most widely used for massive data processing.
  • Python: The most popular programming language in the field of Big Data. 

How to Become a Data Engineer in 2022?

To become a data engineer in 2022, you can enroll in the best data engineering courses online or try this road map which is one of the most efficient paths.

  1. Learning Plan

There are multiple paths to becoming a data engineer, but the first and perhaps most crucial step is to define what you will learn, how long you want to learn it, and what you want to achieve with it. The help of a comprehensive idea of what you want and need to become a data engineer will make the learning path much easier for you. 

  1. Gain Knowledge & Skills

Data engineering is relatively new and not as popular as data science. And considering that the world produces over 100 trillion bytes of data per day, data engineers are in demand now more than ever. 

Engineers use several programming tools, including cloud-based tools, to develop an architecture that can process a large amount of data, making it ready for use by other professionals and departments in the organization. Therefore, data engineers must work with big data processing tools. 

Similarly, these professionals must be kept in constant training. Also, it would be great if they knew how to easily write code in any language, review it, and find errors. Simply put, they must be passionate about data and prepared to work with diverse technologies. 

Since it’s still a new career, hardly any colleges offer majors with data engineering programs. However, digital learning is way ahead in this regard, with some of the best data engineering online courses to learn data engineering in a more affordable and accessible way. 

Conclusion

This article explains why it is a good time to study Big Data and how to become a data engineer. At Hero Vired, we provide the best data engineering courses where you can learn everything you need to become a data engineer. In addition, we also host a variety of other online courses that can help you speed up data analysis tools, databases, programming, and more. Our boot camps also provide you with certifications that you can use to get your first job.