5 Essential Skills Needed to Become a Big Data Engineer

It can be rightly said that big data is the new mainstream across all high-performing industries. Prominent enterprises now base their decision-making skills on insights derived from the analysis of big data. The fact that big data gives you an edge over competitors is as much true for enterprises as it is for professionals working in the analytics domain. Big data brings forth an ocean of opportunities for those who like to work with numbers and are passionate about unearthing patterns in rows of raw, unstructured data.

When it comes to big data professionals, job roles like data scientists, machine learning engineers, and data architects instantly flood our mind. However, another upcoming job role is now attracting attention among recruiters, that of a big data engineer.

What is Big Data Engineering?

Before we delve into what big data engineering is, it is important to understand what constitutes big data. It is a collection of larger and more complex data sets, particularly from new sources. These data sets are so intense in their volumes that traditional data processing software find it difficult to manage them. Gartner defines big data as the data that contains greater variety arriving in increasing volumes and with increasing velocity. These are the three Vs of big data, i.e., variety, volume, and velocity.  


Big data processes high volumes of unstructured, low-density data. The data can be of unknown value and can come from a variety of sources such as social media, business sanctions, and information from sensors and machines. Some organisations may have terabytes of data, for others, it could be several petabytes.


Velocity defines the fast rate at which the data is received from the sources. Usually, the highest velocity of data gets streamed directly into the machine’s memory as opposed to being written onto the disk. However, some internet-based smart solutions can operate in real time and perform quick evaluation and action.


Variety is concerned with the different available data types. While traditional forms of data were well structured and could be constituted into a relational database, big data usually comes in new unstructured forms.

Big data engineering is a specialisation that qualifies engineers to work with big data. It involves developing, maintaining, testing, and evaluating big data solutions. Big data engineers are trained to understand real-time data processing, offline data processing methods, and implementation of large-scale machine learning.

Since big data engineering is a demanding specialisation, having sufficient experience with software engineering is a prerequisite to enter the field. In addition to this, a familiarity with coding and testing patterns, object-oriented designs, as well as experience working on open source software platforms would give students an additional benefit. It would be even better for them to have expertise in NoSQL and data warehousing as well.

Big data engineers are tasked with building massive big data reservoirs and highly scalable and fault-tolerant distributed systems, that can inherently store and process massive volumes or rapidly changing data streams. They are also responsible for developing, constructing, testing, and maintaining frameworks like large-scale data processing systems and databases. Once data flow is achieved from these pools of filtered information, data engineers can then incorporate the required data from their analysis.

What is Big Data: Types, Characteristics, Benefits, and Examples


5 Skills One Should Pick Up to Work in the Big Data Space

True to its name, big data is a vast domain that covers a variety of skills. However, to get the most out of your big data engineering course, investing in these five skills will give you the necessary kickstart to enter into the big data space:

  • Apache Hadoop:

Apache Hadoop has seen tremendous development over the past few years. Its components like HDFS, Pig, MapReduce, HBase, Hive, etc., are currently in high demand by recruiters. Although Hadoop is now almost a decade old, many software companies are still heavily relying on its clusters due to its ability to deliver perfectly mapped results.

  • NoSQL:

NoSQL databases like MongoDB, Couchbase, etc., are now rapidly replacing traditional SQL databases like Oracle, DB2 etc. This is because NoSQL databases are better equipped with meeting big data access and storage needs. In addition to this, their data crunching ability also complements Hadoop’s expertise. So much so, that big data engineers with expertise in NoSQL are in immediate demand in most places.

  • Setting Up Cloud Clusters:

Given the acute reliability that big data places on networks, a lot of work is outsourced to the cloud to avoid the hassle. To accommodate the wide volume of big data, several cloud clusters are set up depending on the organisation’s requirements. Not only does the elasticity offered by cloud makes it ideal for big data engineering, but cloud clusters also make it easier for engineers to crunch large volumes of data to discern patterns. Being well versed with setting up cloud clusters can give tremendous growth opportunities in prominent multinational companies.

  • Machine Learning:

Even though big data engineering has a vast landscape, machine learning and data mining make an essential contribution to the field and are some of its most prominent components. There is still a scarcity of professionals that can effectively use machine learning for carrying out the prescriptive and predictive analysis. Developing expertise in these fields can help big data engineers in developing classification, recommendation, and personalisation systems. These engineers are in high demand in service-based companies like Netflix, Amazon, Spotify, etc.

  • Apache Spark:

In addition to the Hadoop framework, Apache Spark is also extremely popular in roles involving big data analytics. A quicker and more straightforward alternative for complex frameworks like MapReduce, many organisations are now expanding their operations and looking for professionals with experience in Spark. Moreover, the increase of Spark’s in-memory stack has also made this skill extremely sought after by headhunters of prominent consulting firms.

Role of Apache Spark in Big Data and What Sets it Apart


Growth prospects

Even though organisations generate multitudes of raw data, it would hardly be of any use to them without the skills to analyse it. This is where big data engineers come in the picture. From a career perspective, there is little doubt that big data engineers will have a positive growth curve. The worldwide revenues for big data services are expected to increase from $42 billion in 2018 to $103 billion in 2027 and are projected to attain a Compound Annual Growth Rate (CAGR) of 10.48%. As far as the market is concerned, the global big data market would achieve a net worth of $31 billion by the end of this year, thus documenting a growth of 14% from the previous year. There is an escalating demand for big data engineers. Glassdoor itself has listed about 107,730 big data engineering jobs in the US alone.

Job Market

One of the most preferred job roles of our times, big data engineers have an annual salary growth of about 9%. The average starting salary of a big data engineer can range from INR 6,00,000 to INR 10,00,000. According to a survey performed by the Internal Revenue Service (IRS), the top salary bracket makes big data engineers the top 5% of the highest earning roles. According to a study performed by Accenture, 83% of the world’s enterprises have now started pursuing big data projects to gain a competitive edge. An increasing number of enterprises have now started adopting big data in their projects, while others have already made plans to incorporate big data in their future projects.

The sports industry, for instance, has an increased demand for big data engineers to track metrics of consumers like their social media behaviour, ticket-purchasing habits, demographics, brand interests, and psychographic profiles. As organisations get particular about the data they infer and collect, big data engineers are increasingly being demanded by recruiters.

Now that it has been established that big data engineering has excellent prospects in the future, the next step would be to get enrolled in an excellent big data engineering course. To help you with that, UpGrad has now launched a one-of-its-kind PG Program in Big Data Engineering in association with BITS Pilani. This eleven-month course would first introduce students to the foundations of big data, and will then progress towards teaching them more advanced topics like ETL and batch processing, real-time data processing, and finally culminating into big data analytics and a hands-on capstone project. The program ensures hands-on training in industry-relevant tools such as Hadoop, Sqoop, Flume, Oozie, Kafka, Storm, Spark and others. The entire course lectures will be delivered by industry experts and the incredibly talented faculty members of the BITS family.

Big Data Roles and Salaries in the Finance Industry


Big data is an upcoming field that is expanding its application into virtually every industry. For this reason, there is an increased demand for engineers who can work with big data in almost every big industry player. Companies like Cognizant, Deloitte, Accenture, Snapdeal, Flipkart, Amdocs, MuSigma hire big data professionals at attractive salary packages. Do you see yourself working as a big data engineer in the future? If yes, then what are you waiting for?

Facebook Comments

Plan Your Big Data Career Today

BITS Pilani's PG Program in Big Data Engineering in association with UpGrad
Learn More