Big data engineering –a quick guide to a coveted career

Big data engineering is not a new field has emerged from the background in recent years and started to take center stage. It is a practice of building and designing systems for gathering, storing, and evaluating data at a larger scale. Companies tend to gather massive amounts of data, and they require the right people and technology to make sure the gathered data is in a highly usable state by the time it is used by data analysts and data scientists. Those right people are none other than big data engineers.

What do big data engineers do?

Big data engineers are critical to the process of data science. They work in different settings to build systems that gather, manage, and collect raw data into useful information for big data analysts and data scientists to interpret. The roles and responsibilities of these engineers vary depending on the company’s level of data maturity and staffing. However, there are a few tasks that are foundational to the role of big data engineers such as:

· Collaborating with the team and management to understand organizational objectives.

· Developing algorithms to transform data into useful information

· Moving data from one format or system to another.

· Extracting data from the source, modifying it, and then placing the data in a location where users can access it and know that it is production quality.

Why should one pursue a career in big data engineering?

A career in big data engineering can be challenging and rewarding. A big data engineer plays a vital role in a company’s success, providing easy access to data that decision-makers, data scientists, and analysts need to do their jobs.

As long as there is data to process, big data engineers will be in demand. A report by Dice Insights released in 2019 shows that big data engineering was a top trending job in the technology industry and it continues to be one.

Salary of big data engineers

The average salary of big data engineers is USD 111,933, with some of them earning as much as USD 164,000 per annum (Glassdoor, 2021). This shows that data engineers are compensated well as compared to other data roles such as database administrators or managers.

How can one become a big data engineer?

Individuals can advance or launch a rewarding career in big data engineering with the right set of skills and knowledge. Many of them hold a bachelor’s or a master’s degree in statistics/computer science or related field which helps to build foundational knowledge in this quickly-evolving field,

Besides this, there are several other steps that individuals can take to get your foot or look for advanced opportunities in this field such as:

Developing big data engineering skills

Individuals can learn the fundamentals of programming languages, databases, and data pipelines during the initial phase of their careers.

Programming languages

A strong foundation in SQL, Java, Python, and Scala allows data engineers to optimize queries for data transformations.


Learning MySQL, Oracle, Microsoft SQL Server, and many other databases will help data engineers to record transactions.

Data Pipelines

Combining processing engines, programming languages, and transactional databases results in pipeline. These pipeline skills will allow data engineers to bring data to one place in business intelligence for informed business decisions.

Earning big data engineering certifications

Certifications can validate an individual’s skills to potential employers, and preparing to earn a big data engineering certification is an excellent way to develop the aforementioned skills. Some of them to earn are:

· IBM Certified Data Engineer

· Associate Big Data Engineer (ABDE) by the Data Science Council of America (DASCA)

· Google Cloud Certified Professional Data Engineer

· SAS® Certified Big Data Professional using SAS® 9

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AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

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Albert Christopher

Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

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