If the goal of businesses is to make profits, the role of a Data Engineer is to ensure that data-driven operational efficiency and business insights help achieve that goal.

The primary task of a Data Engineer is to design and build systems that will collect, store and analyse massive amounts of data in seconds, if not in nanoseconds and make it available for the end user. Such systems now have applications in every single industry we know, in companies ranging from small start-ups to large Fortune 500 companies.

Collecting and reporting data with available software tools is a given. The next critical step is to deliver business value from that data – which requires many nuances to be traversed by an organization to arrive at a meaningful data analytics and data science ecosystem. These stages include transforming data, setting up data pipelines that automate these transformations, setting up processes to constantly track for data integrity, and managing the whole architecture across the enterprise, to ensure that data feeding all the analytics and data science, as well as all the products and services for the business,  helps deliver meaningful outcomes. This is what is collectively called data engineering. According to the 2020 U.S. Emerging Jobs report, Data Scientist and Data Engineer roles are growing steadily, with an average annual growth of 35%. LinkedIn's India portal currently has over 30,000 job postings for Data Engineers of all levels.

Difference between a Data Scientist and  Data Engineer

Data Engineers build systems to collect, validate, and prepare high-quality data that can be used by Data Scientists to predict trends, provide valuable insights and promote better business decisions.

Data Engineers are involved in:

• -Acquiring and building datasets that align with business needs
• -Developing algorithms and tweaking them to transform data into useful, actionable information
• -Building, testing, and maintaining database pipeline architectures

-Collaborating with management to understand company objectives

DEs these days are both enablers and consumers of machine learning systems. In addition to managing regular sized data, DEs today need to manage big data ecosystems that feed machine learning workflows, near real-time, and use various matching learning algorithms to do it effectively

Role of Data Engineers

Data Engineering is crucial to the success of any organization, which is why engineers are paid well. Today, the average salary is around 17 lakhs per annum, but this can vary depending on an individual's experience and previous achievements, ranging from 3.5 lakhs to 21.2 lakhs.

Data Engineering isn't always an entry-level position. Many Data Engineers begin their careers as Software Engineers or Business Intelligence Analysts. One may advance into managerial roles or become a Data Architect, Solutions Architect, or Machine Learning Engineer.

 Where to study from?

There are several Data Engineering programs to help professionals pursue their careers. Always look at the curriculum of the course before enrolling and ensure that along with the foundational syllabus, it should also include the latest tools and technologies that are being used in the industry. Considering the technicality of the domain, it is advisable to consider courses with a comprehensive capstone project that encompasses a rigorous employment of all the tools and techniques learnt and  also provide access to industry experts and mentors.

In addition, many of these programs are also offered in combinations with other technical courses such as Cloud Computing, Artificial Intelligence, Internet of Things, SQL, MongoDB, Bigdata Hadoop, Python, and other related topics – all of which are essential if you want to be a good Data Engineer. If you already have coding skills and are good at database design, they can be built upon for further specialization.

The goal of Data Engineers is to create a data layer that can be used by Analysts, Data Scientists, and other businesses, so that business decisions can be taken in the most efficient manner.

Author:

Hari Krishnan Nair, Co-Founder, Great Learning