Data Engineer: Skills Required and How To Become One?

The world is generating enormous amounts of data. We live in a digital age where every possible thing is accessible through the internet. Back in the 90s, when the world was still going at its traditional pace, the whole business industries were based on traditional marketing and sales skill. With the inception of the internet in the early 80s, the world started changing its pace.

Incomes the age of information, as the sharing and feeding of information begin at a rapid rate due to its availability for free on the internet.

With this kind of growth, businesses around the world started to be more technology friendly. They started taking a consumer-first approach. Have we noticed that while we are sitting on the internet, exploring information, how this data is being generated and what difference it can make to the real world?

As internet generated opportunities for so many businesses to go online and demonstrate their product or services according to the needs of customers. The world is started getting fed with this trend. Nowadays we see different online, and social media platforms where data is getting exchanged at a rapid rate.

Now that we have entered a communication advanced age. Where the generation and transfer of huge data are coming into place. With this technological advancement, there was a need for a branch of science that must be capable of dealing with this enormous amount of data.

Data engineers came in to picture, and the handling and use of this large amount of data in a productive manner became possible. It is a practice of designing and building some systems which can be used for collecting, storing, and then analyzing that data. Every industry in the business world is using the services of Data Engineering. The organization has gained the expertise to store large amounts of data which they are collecting on their portals or through social media.

The end customer is sharing information about their likings, demographics, needs, etc. They need the right people who are technically very advance to handle this kind of data so that they can identify a trend and forecast it for the future by taking some advance and precise business decisions.

What does a Data Engineer do?

Data is considered no less than a currency nowadays. Data is very critical for businesses around the world because it may help them to take various precautionary measures, business decisions, and well-informed predictive decisions. For doing all this, they need the right technically advanced people who can help them understand and interpret this data.

In the whole Analytical team, the Data Engineer plays a very important part as they acquire all the data as it is or which is raw in format. Now they build a system that reads and arrange this jumbled data finally as they initially sort out this data, they send it further for more analysis by Data Scientist.

What is the difference between Data Pipelining, Lake, and Warehousing?

As the data engineer gathers the raw data by accessing various databases the clutter of this data is known as Data Pipelining. This huge amount of data floating in the system which is unsorted for now is known as Data Lake. The sorted-out data which begins to shape out and make out meaningful data is called Data Warehousing.

Data Engineer skills and job responsibilities

Being a Data Engineer, you have to deal with enormous raw data throughout according to your job description. The main job responsibility being a Data Engineer is at the very initial stages of all Data Analytical teams.

• A Data Engineer is responsible for collecting all the raw data. They may go to different databases table to collect all these.
• After successfully retrieving the data, they build a system that manages all the information.
• After managing all the data, they further convert that raw data so it can be interpreted by Data Scientists and Business Analysts.  

Other roles and responsibilities of a Data Engineer

• They have to set up a data architecture, set up a hierarchy where they fetch the necessary data, manage it and keep it aligned with all the business requirements.
• The main question that arises here is how a Data Engineer collects the data. They may utilize different programming languages and tools and obtain the data by going to different tables from different sources. They need to find the right source to extract the data required.
• As a Data Engineer, you always have to be on your toes to simplify any rising business problem. For that, you need to regularly update and do the required research.
• There are various machine learning algorithms like decision trees, linear and logistic regression, or SVM (Support Vector Machine) algorithms that Data Engineers must keep themselves up to date. There are various other pictorial representation tools like Tableau and Apache Spark, they should be well aware of and proficient in them.
• Data Engineers use various descriptive and predictive models for data aggregation to extract the data and to study some historical trends around it. 
• Data Engineers also use prescriptive models that allow the users or stakeholders to take advantage based on the analysis of the data. Data Engineers are also responsible for streamlining and solving all the hidden patterns in raw data.
• Another important and time-saving skill of a Data Engineer is to reduce the manual effort that is required during fetching and managing it by simply automating the whole process.

Values and importance to be a Data Engineer

• In an organization, the post of a Data Engineer holds a very important place. Analytics is a branch that helps the majority of the business and its different branches like Sales, Marketing, Customer retention, or maybe operations. 
• Data Engineers are believed to extract and interpret the different data sources by using databases of SQL Server, Oracle DB, MySQL, or MS Excel. Once data is retrieved from all these databases, they may apply different algorithms to make it useful so later they can assist other business departments like sales, marketing, and finance with that.
• Data Engineers are believed to carry the analytics department of the organization. They add meaning to the collected raw data. 
• The chief goal of being a Data Engineer is that help businesses make decisions by handling real-time data by accurately and precisely predicting or forecasting customer trends, viral factors, retention, etc. 
• If I tell you this in a simplified language the Data Engineer helps different organizations around the by understanding and magnifying the trend, they have received from the data they have collected from different databases.

How to become a Data Engineer?

• As this domain seems very dreamy to all of you, but it requires work on your analytical approach along with your computer language application and your technical savvy approach.
• First, you need to get enrolled in a bachelor’s course in Technology (Computer Science), Mathematics, or any IT-related course. Though there are various certificates that can help you to enter this field of study.
• A Data Engineer should know how to perform a comparative analysis of data stores. Should be well aware of data terminologies while maintaining it like Data pipeline, Data Pool and Data Warehousing.
• As a Data Engineer, you have to be proficient while handling relational and non-relational databases for that you should be well aware of SQL and NoSQL domains.
• As the demand for being a Data Engineer rises abruptly, many online courses that are certified by the companies like Google, Oracle, IBM, and Amazon have been readily available in the market. Taking these courses will help Data Engineering aspirants.
• Once you have gained the necessary skills, knowledge, and experience after doing all the certifications and elementary bachelor courses. You can start building your portfolio. A portfolio is a way to demonstrate the knowledge and skills you have utilized while working in an elementary position may be as an intern.
• Once the recruiter is impressed with your knowledge and your dedication in this field, you are more than eligible to get a job in the field of analytics.