Data science has become a huge element of the modern business world. There are many roles and career paths that have emerged from this field. Data engineering is one of the most important and fast-growing roles in the data world. If you are interested in data science, you should learn about data engineering and how it plays into modern data operations.
Estimated reading time: 3 minutes
What is data engineering?
Data engineering is the practice of designing and building solutions for collecting, organizing and using data. In other words, data engineers help to build the data pipelines that serve information to data scientists, business analysts and other end-users to help them make informed decisions. In many cases, it focuses significantly on data automation, wrangling, cleaning and organization.
If you are interested in data and computer science, data engineering may be right for you. It is a field that involves lots of problem-solving and the creation of useful solutions.
What do data engineers do?
As mentioned, the main role of data engineers is to design and build data pipelines. Depending on the size of the team, this may be under the guidance of a data architect. For smaller teams, the data engineer may play both of these roles.
The goal of data pipelines is to ensure that the people and processes using data have thorough, clean and well-organized data to work with. Some pipelines may feed directly into computer systems such as enterprise resource planning solutions. Others may organize data in a database but leave it up to the end users how to manipulate and display that data.
The exact role of a data engineer depends on the requirements of the organization. Ultimately, the purpose is to use computer science to enable the organization’s data goals and processes.
How do you become a data engineer?
In most cases, data engineers have degrees and experience in computer science and related fields. Since the modern iteration of data science is quite new, there isn’t always an expectation that engineers have direct education or experience. However, it is helpful to at least have a strong foundation in data structures and the tools used to manage them.
Knowledge of programming languages is essential. This includes primarily Python and Java, although there are many other languages that may be used. Additionally, knowledge of data querying languages such as SQL is important.
It may be helpful to pursue an advanced degree in data analytics. However, this is not always necessary to get started in data engineering.
What are the challenges of data engineering?
One of the most significant challenges faced by data engineers is the newness and rate of change of the data science field. There are constant innovations. This can make it very exciting, but there is also a lot to keep up with.
Additionally, data operations in a business sometimes form a sort of shadow IT system. Although having direct control of your systems can mean a lot of flexibility, it can also bring up challenges with maintenance and compatibility with other systems.
What are the possible advancement paths for a data engineer?
Data engineers have a lot of potential advancement opportunities. Naturally, one of the most common options is to become a senior engineer, data engineering manager or director of data engineering (or possibly all three in a sequence of advancements).
Another common route is to get started with data architecture. This builds on many of the concepts used in data engineering but is more focused on planning and devising data structures, rather than implementing plans. Some data engineers may move into data consulting roles. Since it is such a big force in the business world, many organizations are looking for experienced help.
Discover more about data engineering today. With the above information in mind, you can start planning your possible career in this field. Alternatively, you may find that a different data role is right for you.
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