The decade of the 2010s was the decade of data. Data began to be used in nearly every sphere of life, and companies began to understand how data can completely transform every avenue in which they work. Companies that became front runners in using data to govern their decisions also turned into front runners of their respective fields. Data became all-pervasive, and the role of a Data Analyst, Data Scientist, or Data Engineer, became among the most coveted roles for all types of people, whether new graduates or experienced professionals.
If the 2010s were the decade that heralded the emergence of data, the 2020s would be the decade that will firmly establish its role in every aspect of our lives. Over the next few years, nearly every one of our acts involving even the smallest amount of digitization will involve the role of data in some way or the other. Data will be all around us, and the need to learn more about it will be greater than ever. In such circumstances, the roles and responsibilities of Data Engineers will increase to a greater extent than ever before. This article deals with Data Engineer roles and responsibilities in 2021.
Data engineer duties already encompass a wide variety of jobs in any company. In 2021, the roles and responsibilities of Data Engineers are only bound to increase. Below are the roles and responsibilities of Data Engineers:
Working on the data architecture in a company is a major part of the roles and responsibilities of Data Engineers. The data architecture of a company or firm includes the policies and methods using which the data associated with its projects is collected, stored, analyzed, and retained. It is very important for any Data Engineer to ensure that the company’s data architecture remains intact and no untoward incident causes the loss of data to the company. The Data Engineer is not just supposed to main data architecture, but also to keep on updating it as required.
Among the most difficult and banal tasks for a Data Engineer is the task of collecting data. Data collection can involve a host of different activities, including going to every manager or responsible person in the firm and asking for data on a regular basis. Any Data Engineer should always know the appropriate resources from which to procure data. A number of resources can yield faulty or incomplete data, and Data Engineers must know the correct sources from which complete data might be readily available.
The role of a Data Engineer often seems to be just the analysis of data. However, simple data analysis has a few prerequisites. Among the major prerequisites of the analysis of any data is to know the domain to which the data is related. Very often, Data Engineers will be faced with data from a field outside their core domain. In order to be able to analyze this data effectively, it is essential for the Data Engineer to conduct research about the field. This doesn’t just enable the job to be carried out well by the Data Engineer but also helps in better visualization of the results.
The skills required to become a competent Data Engineer are only increasing every day. Every month, researchers come up with new methods that can enable engineers to analyze data much more effectively. The only catch is that these methods often need to be learned from scratch. In fact, a number of these methods can be based on programming languages that the Data Engineer doesn’t know. It is hence important for the Data Engineer to always be updated with the latest technology and skills. In Data Engineering, the skills required to be good at one’s job grow redundant fast, and hence continuous learning is among the responsibilities of a Data Engineer.
This is among the main functions of what Data Engineers do. For any Data Engineer, it is not only important to be able to analyze data using code but to be able to analyze data in a meaningful fashion. This means going through the data and the results you have procured from your analysis and then being able to identify patterns in the data. Based on these patterns, you can create proper data models that make the understanding of your results easier for a layperson. The creation of new models is counted as the pinnacle of Data Engineering.
In a world where everyone has too much on their plate, it falls upon Data Engineers to determine how such burdens might be reduced. Data engineers need to find out the tasks in every project that can be totally or partially automated. They then need to take requisite steps to automate these tasks, reducing the manual intervention in them. Reducing manual intervention doesn’t just reduce the work burden on team members but also makes a large number of tasks more precise and accurate.
Data engineers are involved in every important task in any organization. They are involved in the analysis of the voice of customer and voice of business data, helping the design and product management teams understand the need for a particular product, as well as what the people really want. They can help all the departments of a company in some way or the other. They can help multiple teams determine the efficacy of their actions; for example, they can help marketing teams understand whether a particular marketing campaign has been successful or not.
Furthermore, once a product has been launched, Data Engineers can analyze customer data and find out the feedback of the customers related to the product and whether these customers are likely to be retained. Additionally, they can use advanced data metrics to forecast the future of products helping senior management make decisions on whether to continue a product or not.
Becoming a Data Engineer by qualification can be an easy task, but becoming a real Data Engineer requires a significant amount of training as well as independent study. By qualification, the easiest way to become a Data Engineer is to enroll yourself in a bachelor’s degree in computer science, information technology, or mathematics. Each of these degrees teaches you some common subjects that are really essential in becoming a great data scientist. You can also gain certifications in data science, which are offered by universities across the world.
However, to become a competent Data Engineer also requires a lot of independent analysis. Data science is one field where you can learn a lot of skills yourself, even without a mentor, and then apply them to problems devised by you. A lot of data is available in online repositories for you to analyze and work on. Additionally, you should also be good at subjects such as data structures, data warehousing, and database systems. The design of relational and non-relational databases must be at your fingertips, and so must be domains such as SQL and NoSQL.
There are a number of skills you can acquire to better enable you to become an effective Data Engineer. Coding is an essential tool in what Data Engineers do. Here are the main subjects you might wish to know:
For Data Engineers, knowledge of SQL is the basic level of competence required. SQL allows Data Engineers to analyze data in an effective manner through easily understandable syntax and the ability to manage relational databases. In fact, there is no way that a Data Engineer can be great at relational database management systems if not knowledge of SQL has been acquired.
Being a Data Engineer is not just about collecting and analyzing data. Data engineers must also know how to store data in a structured manner. Most of the data that Data Engineers will receive will always be unstructured and raw. What’s more, they may also receive data from disparate sources. Data warehousing is the field that deals with restructuring this data into a meaningful format. It is only once data has been structured and warehoused that it can really be used for any type of analysis or modeling. Data warehousing also helps future Data Engineers access data in an easier manner.
Data engineers must have in-depth knowledge of data architecture. This entails knowledge of all the processes that data undergoes from its acquisition to its analysis and retention. Data architecture also includes the policies that need to be followed to carry out each of these processes. Every client that you will work for will have different demands with regard to data architecture, and you must be competent enough to be able to fulfill them.
There is no way that you can be a successful Data Engineer if you do not have knowledge of coding. Simply analyzing data on readymade suites and commercial software does not make a Data Engineer. In fact, as a Data Engineer, you probably need knowledge of multiple coding languages. The preeminent language for data analysis in R and Python is also used widely for studying data. Apart from these languages, traditionalists also use C and C++. You can be required to use a host of different languages for data modeling purposes, and you must ensure you are up to the task at all times by knowing more than one programming language.
The operating system on which you work is often ignored when it comes to the skills of a Data Engineer. We are so used to working on the Windows operating system that we tend to forget about the existence of other, specialized operating systems. A good Data Engineer will generally have knowledge of multiple operating systems, such as Unix, Linux, and Solaris, apart from Windows or macOS.
Machine learning is not just a 21st-century buzzword but has a number of real applications. Machine learning is based on the analysis of data, and a Data Engineer must know the algorithms and models that govern any machine learning problem. Data engineers can easily branch out into machine learning if they know the fundamentals of the field.
Data engineers will only be growing in demand in 2021, and it is hence important that they understand the roles and responsibilities of Data Engineers. This is also important for budding Data Engineers, just as it is important to understand the skills and qualifications necessary to become a Data Engineer. The above lists are bound to help you in your future career and help you diversify into the wide world of data analysis.
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