What Skills do Data Engineers Need?

If you are confused and troubled about which career to choose to make your dreams come true for your life, then you dive into the world of data engineering. In this big data world, every task is in one or more ways connected to the use of digital methods. To organize, store, manage data, a company requires more Data Engineers to perform these tasks for them.

Data engineering can be defined as building and designing systems to perform storing, collect, and analyze data at a very large scale. This is a broad task in every field or organization. The ability of an organization to collect massive information and to handle it, people are required with appropriate technical skills to ensure the safety and security of data till it is reached to the data scientists and transformed into a highly usable form. Data engineers are responsible to convert the raw and barren data into a usable and utilizable form. One can take a Post Graduate Program in Data Engineering to start a career in this field.

Responsibilities of Data Engineer

When we talk about an organization’s growth and improved structural chain the information or data play the most important role due to which a data engineer possesses a great priority, he is responsible to deal with data and he is the heart of an organization. The preparation,  handling, and organizing of raw data is their primary responsibility along with the maintenance of numerous business-oriented data as per their company’s requirements. They help to overcome the obstacles and propose solutions for business problems. They analyze and visualize data with the help of various reports, dashboards, and graphs. They extract data and further make them in a usable form for processing like analyzing and modeling.

There are a number of skills required to maintain all such responsibilities of a data engineer and take care of the organization’s improvement and growth. A Data engineer must possess skills that are mentioned as follows:

Machine Learning

Machine learning refers to the use of all the latest tools and algorithms through which one can create the results using historical data and present data. Nowadays machine learning has been one of the most popular technologies to be known for the past few years. Data engineers must have knowledge of machine learning and all its algorithms through which they can easily understand the organization’s requirements and can have the ease to communicate and give explanations to data scientists and analysts. Apart from this, it would help in creating better data pipelines and modeling structures.

Data Structures

The Data engineers although perform data filtering and optimization, it would be very beneficial for them to be familiar with the basics of data structures. It will help and assist you to better understand the organization’s aims and every aspect including the cooperative working with the other members and teams in the organization.

Data Warehousing

The process in which you can store a large amount of data for analysis and queries is known to be data warehousing. Data is obtained from various sources such as accounting software, CRM solutions, and ERP software. The organization uses this data to generate various reports, perform analytics and data mining to create valuable insights. Data warehousing is very important for the data engineer to be familiar with. They must also be familiar with the tools used in data warehousing.

Programming Languages such as Java, Python, Scala.

There are various programming languages such as Python, Java, Scala, etc. They all have their significance in data engineering. Python is the most popular and preferable language as it deals with all statistical analysis and modeling. On the other hand, Java is a very helpful language when you work in data architecture frameworks, whereas Scala is one that is an advanced extension of it. One should be well trained with Python and its coding concepts. There are many other languages such as. NET, R, Shell Scripting, and Perl. Java and Scala use a vital Hadoop component and work with MapReduce. You must have mastery over any one of the languages discussed above.

ETL Tools

ETL refers to the Extract, transfer, Load and also denotes how the data received from different sources is extracted, transformed, and then formatted to a manner and then stored in a data warehouse. It is the ETL processing that uses the data and produces as per the requirements of the users relative to their specific business problems. The details are received from a number of sources and follow some set of rules to manage the data and load it to the database so that from there organizations can use or view it.

SQL and NoSQL

SQL and NoSQL are the two must-have qualities of a data engineer. SQL has stayed as the primary programming language creating and managing the relational database management systems(RDMS). Relational databases management systems are very popular and contain different rows and columns. NoSQL, on the other hand, is the nontabular form of database representation that varies from model to model. You must possess both database management systems including SQL and NoSQL to be a data engineer.

AWS and Azure

AWS refers to Amazon Web Services which is the topmost used tool for data warehousing. A data warehouse can be defined as a relational database that focuses on the analysis and queries to help you out for a long-range view of information. AWS is a cloud-based platform that enables you to access various tools of data engineering. Similarly Azure is also a cloud-based technology that helps to provide large-scale analytics solutions. It has various solutions such as IaaS(Infrastructure as a Service), SaaS(Software as a Service), and PaaS(Platform as a Service).

 

If you are starting your career as a data engineer, then you should have the basic skills that are mentioned above in this article. All such skills are the fundamentals of a data engineer profession.