Data Engineering: An overview on the importance of this fascinating role
By Dimitrios Koulialias - 15.10.2022
In our nowadays fast-paced world, the global amount of data has been vastly increasing over the past decades. This evolution has brought up several changes for companies and their employees, most notably that it has created new data-driven job opportunities, such as those for data scientists and data engineers. While the former extract deeper information out of their datasets using state of the art technological tools (e.g., data visualization, machine learning, AI), data engineers play an equally important part in providing them with the necessary conditions in terms of data collection, management, and quality, such that they can be readily used to meet specific business needs.
The purpose of this blog article is to give you an overview on the key aspects associated with data engineering. Even though my knowledge on data engineering is limited at the time of writing this article, I would like to share my personal view on this subject, based on the first experience I am gaining in the context of a client project at my current employer NeoXam.
Popularity of the data engineering role
Owing to its highly interdisciplinary nature and the increasing need of the industry in generating reliable data infrastructures, it cannot be argued that the number of data engineering roles are currently exhibiting the highest growth rate among other tech occupations. According to the DICE’s job report, the year-over-year (YoY) growth in the number of data engineering roles has amounted to 50 % in the year 2020 (Figure 1). This popularity can be further linked to the evolution of the number of professionals that are entitled as data engineers on LinkedIn. Since 1995, this evolution follows a nearly exponential behavior, reaching a total number of about 6000 data engineers as of 2015 levels (Figure 2).
The increasing popularity for data engineering can be largely explained by the rich skillset that qualified engineers bring with them, especially the ability to understand data needs and to provide all necessary means in terms of infrastructure and data quality, in order to translate datasets into actionable insights (see also section ‘Data Engineer responsibilities and requirements’). Moreover, a further factor that makes the role of a data engineer appealing is that it corresponds among the highest well-paid tech positions, yielding an average income of about 116’000 $ p.a. for the US, and around 60’000 € p.a. for Europe, respectively [3] (as of 2019).
Given the above and taking into account the outlook on the amount of computer and information research jobs that is generally estimated to increase by 21 % in the current decade [4], it can be assumed that the demand of companies for qualified data engineers will continue to remain at high level. In the following, let us have a closer look on a data engineer’s responsibilities and requirements.
Data Engineer responsibilities and requirements
Considering the job advertisements scattered across manifold platforms throughout the web, the responsibilities and requirements for a data engineer are defined in a relatively broad manner. This is due to the fact that the exact knowledge and skillsets largely depend on the business requirements and therefore, they often vary from one company to another. As a result, the list of requirements might at first seem quite overwhelming, especially from the perspective of a new or a graduate data engineer. Nevertheless, given the state of the art programming languages and technological solutions that are nowadays available, there is general consensus in the community that the skillset of a data engineer can be summarized to the following key requirements (e.g., see Ref. [5]):.
- Having a thorough understanding of programming languages with a main emphasis on Python and SQL
- Having a first experience in the design, implementation, and maintenance of database systems
- Experience in processing, transforming, and loading datasets of various types into data warehouses and/or data lakes, which generally relates to the area of extract, transform, and load (ETL) processes
- Knowledge of concepts associated with cloud computing
With the above skillset, data engineers have a solid starting point to address a variety of business use cases. These mainly include, but are not only limited to, the integration of data management components into an existing structure, as well as the design of ETL pipelines that transport data within this structure into the desired formats. Moreover, due to the highly interdisciplinary role, data engineers have a considerable degree of freedom to build up their expertise on specific systems and tools they find most appealing.
Finally, apart from the technical skills, it is worth noting that the soft skills play an equally important role. Given that in order to obtain a general understanding of the business requirements from various stakeholders (e.g., data scientists, business analysts), strong communication skills, as well as a solution-oriented working approach within a team, can be considered as must-haves that complete a data engineer’s profile.
Summary
In this article, I provided you an overview on the importance of data engineering. In view of the increasing need for data-centric business approaches, I am convinced that the market potential for data engineers, like for any other data-related jobs, will continue to play an important role in the next years. Moreover, due to the rich skillset and technical expertise, the career path of data engineers can be quite manifold, given that they can contribute their knowledge among a broad spectrum of potential employers, ranging from well-established tech companies to small and medium-size enterprises, as well as startups.