The demand for data professionals has never been higher, especially for data engineers. Forbes has noted that data engineers, especially those that are able to handle big data, rank among the top emerging roles in LinkedIn. But what does a data engineer do and what does it take to be one?
The work involved
Data engineers work primarily on the tech side of the data ecosystem of the organization. To be specific, they build data pipelines that prepare the raw data for analytical or operational purposes. With such responsibilities, they often work with other people working within a company’s data infrastructure system such as the data warehouse engineer, data platform engineer, data infrastructure engineer, analytics engineer, data architect, and DevOps engineer.
With code being a predominant element in today’s technology infrastructure, it is important that a data engineer possesses strong developer and programming skills as they would be required to write coding language.
The data engineer also must have an interest in data. This may seem painfully obvious, but it still needs to be stressed as big data and more complex processes are involved that require more than just a passing knowledge of data.
While not technically considered a skill, having an operations mindset is also important. This means being on guard to ensure the data systems are operational at all times, as well as ensuring the reliability of the infrastructure that can withstand unexpected issues that may arise within the system.
Lastly, having social and communication skills is important as well. A data engineer serves internal teams, so they must understand the business goal that the data analyst wants to achieve to best support them. Also, as was touched on before, the data engineer’s work involves a lot of collaboration, so it is important to communicate clearly what they will be doing, especially considering that not all the people they will be working with do not speak their technical language, so to speak.
There is no particular degree or certificate required to become a data engineer. But it is important for to be knowledgeable of SQL, Python, R, and ETL methodologies and practices. Data engineers should also be knowledgeable of 10 to 30 different technologies in order to determine and apply the right technology as required by a particular process. These technologies include Apache Spark, Apache Hive, Hue, Heron, and Apache Kafka just to name a few.
Java Is another programming language used in data engineering, however, the specific Java knowledge being sought for data engineering is vastly different from what is being taught in the IT academe. This brings up a more important aspect for an aspiring data engineer.
Experience is necessary
More than the aforementioned knowledge of different programming languages and tools, as well as the developer and programming skills, a data engineer needs more to build their experience, especially if they intend to grow and thrive in this role.
In gaining this experience, one does not need to begin as a data engineer right away. Starting as a data analyst serves as a good training ground for one to get a feel for the business value of data before eventually moving down the stack into data engineering. An experience in DevOps or as a site reliability engineer can also help one get into the data engineering field.
Achieving growth as a data engineer
Whatever preconceived notions one might have about being a data engineer, it is considered a difficult job, much more so than being a software engineer. Nevertheless, their work is of critical importance in ensuring the integrity of the data infrastructure of any company, and are considered the “unsung heroes” in data operations.
Data engineering is also a role where one continuously finds growth. That is because there is something new to learn in the field as data practices and technologies constantly evolve. People with a growth mindset will find the role fulfilling as they will find constant growth throughout their careers.
But while the role provides countless opportunities for growth, the data engineer also has to play their part to be able to attain this growth. A sense of awareness and a passion for constant learning is important for the data engineer to be able to adapt to and adopt the changes taking place in the ever-evolving data landscape for their organization and for their self-development.