Data Engineering: A handy guide to this in-demand career

Are you starting or advancing your data science career with the goal of determining what kind of position within this diverse and rapidly expanding subject is the best appropriate for you?

Don’t worry! It's not just you.

You also probably already know that there is a great need for knowledgeable, data-savvy individuals across all industries and that these professionals command premium salaries. There are several reasons to pursue this career path and perhaps even get a master's degree to better position yourself for employment.

Data engineers that can gather and manage massive amounts of data are needed since big data is transforming the way we conduct business. The management, upkeep, design, and building of database architecture as well as data processing systems are now the sole domains of specialist data engineers and data scientists.

Don't worry if you've wondered what exactly a data engineer does at work and have been inquisitive about that career. You can understand it better after reading this blog.

Who is a Data Engineer?

A data engineer's position is quite technical, and data permeates every area of their work. They laid the groundwork for the company's data capture, transformation, and administration.

Data engineers analyze, visualize, and create machine learning models that can be utilized to handle data in the most effective way possible. They play a critical role in bridging the knowledge gap between data scientists and software and application developers.

Additionally, they make sure that the data collecting and storage processes are effectively completed, which corresponds to the first phase of the data workflow.

These engineers must correctly filter enormous volumes of data that have been obtained from various sources and make it available in a raw material format so that data science specialists data scientists and data analysts can continue working on it.

This facilitates the manipulation of huge data sets to yield important insights like availability and performance. They also keep an eye on the systems' processing of the data and how it is flowing and looking.

How to become a Data Engineer?

There are very few colleges and universities that provide courses specifically for data engineering, which is still a relatively new profession. A person who is eager to learn this, however, should normally have a background in data science, engineering, math, or a business-related field.

A successful career in data engineering can also be attained by taking a degree program that includes system architecture, programming, and database configuration.

A data engineer needs to be well-versed in the following for success:

• Programming languages

• Database management

• Stream Processing frameworks

• Distributed computing frameworks

• ETL frameworks

• Cloud technology

As they frequently serve as the "translators" who explain the raw data to diverse stakeholders who have little to no expertise in computer science, data engineers also need to be effective communicators.

You can take numerous additional steps to position yourself for success in addition to getting a degree.

1. Improve your knowledge of data engineering:

To get started on a career in data science, learn the basics of cloud computing, coding, and database design.

Coding - Consider enrolling in courses to develop and hone your coding skills since proficiency in these languages is necessary for this position. SQL, NoSQL, Python, Java, R, and Scala are popular programming languages. ETL (extract, transform, and load) systems - ETL is the procedure used to move data from databases and other sources into a single repository, such as a data warehouse. Xplenty, Stitch, Alooma, and Talend are examples of common ETL tools. Relational and non-relational databases - One of the most widely used methods for storing data is the database. You ought to be knowledgeable about databases, both relational and non-relational, and how they operate. Machine learning - Although data scientists are more interested in machine learning, it can be useful to have a basic understanding of the ideas to better understand the demands of data scientists on your team. Scripting and automation - Because businesses can gather so much data, automation is a crucial component of working with big data. You must be able to build scripts in order to automate recurring tasks. Data storage - Especially when it comes to huge data, not all forms of data should be kept in the same manner. You'll need to know whether to employ a data lake rather than a data warehouse, for example, as you create data solutions for a business.

2. Obtain a certification:

Certification can demonstrate to potential employers that you have the necessary skills, and studying for a certification exam is a great opportunity to advance your knowledge and abilities. Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer are available options.

3. Create a portfolio of work including data engineering:

A portfolio is frequently a crucial tool in the job search process because it demonstrates your abilities to recruiters, hiring managers, and future employers. Your independent or coursework-related data engineering projects can be added to a portfolio website (using a service like Wix or Squarespace).

Numerous data aficionados and engineers have been drawn to specialize in this field by the lucrative and appealing job options. Data engineering is highly sought after, in demand, and gives a lot of room for career advancement for students and professionals who are considering changing careers or looking at their career possibilities.