Data Engineer Salary in India 2022

IT jobs, as you know, are bagging the fattest pay cheques lately, and the same holds for the Data Engineer role. How much is it in your city? Read more to know!

Introduction to Data Engineer Salary in India 2022

In the modern world, when time is measured in fractions of a second, we are always in front of a wealth of information. Search the web for the most recent news, go through social networking sites for the newest trends, and even check your WhatsApp conversations for some fresh, humorous jokes.

This deluge of knowledge is only a luxury when we use it wisely for our benefit and objectives. For continual progress, one should understand all the whats, whys, and how’s of the facts or material next to us. More individuals and strategies are emerging to manage this data correctly as data generation increases globally. The idea of a Data Engineer is now relevant.

What does a Data Engineer do?

Data Engineers create systems that gather, handle and transform unprocessed data into meaningful information that data analysts and business architects may use to analyze in several contexts. Their ultimate objective is to open up data so businesses can use it to assess and improve their performance.

Fundamental Duties of a Data Engineers

The following is a list of the duties that Data Engineers are supposed to perform:

  • Do Research: To solve any challenges that may develop when handling business challenges, Data Engineers carry out studies in the business.
  • Enhance Skills: Data Engineers don’t only depend on abstract database theories. Irrespective of the development platform they are working in or the computer program they are using, they must be capable.
  • Model Building and Pattern Recognition: Data Engineers utilize a descriptive data model to derive contextual information from data aggregation. Additionally, they create forecasting analytics where they are using forecasting methods to understand the upcoming and get insights that may be put to use.
  • Develop your data architecture: They design, develop, and manage data structures systematically, even while maintaining them in line with business needs.
  • Automate Workflows: Data Engineers go into the data to identify processes that may be automated to remove manual involvement.
  • Gather Information: Before beginning any database activity, information must be gathered from reliable sources. Data Engineers then store the processed datasets after creating a set of procedures.

Skills Needed to Become a Data Engineer

The following are the top 6 skills for Data Engineers:

  • Learning Machines: Most often, data science is connected to machine learning. Nevertheless, it will help you in your work as a Data Engineer if you understand how data may be utilized for statistical data processing and modeling.
  • Running OS System: You must acquire knowledge of many operating systems, including Windows, Linux, Luna, and UNIX.
  • The essential knowledge base for Data Engineers is SQL. Without a solid understanding of SQL, you cannot administer an RDBMS (relational database management). You will have to go through a long number of questions to do this.
  • Database Management: Understanding how to create and operate a data warehouse is a crucial skill. Data storage helps Data Engineers combine unorganized data that has been gathered from many resources.
  • Data Structure: Building complicated database systems for corporations requires expertise that Data Engineers must possess. Data operations that deal with data transmission, information stored, databases, and the interaction among data-dependent functions and systems are all part of its scope.
  • Coding: You must develop your programming abilities if you want to connect your databases and interact with websites, smartphones, workstations, and Internet of Things apps. For this reason, learn an enterprise language, such as Java or C#. In contrast to the latter, which can assist users with data science and analytics in a Microsoft-based stack, the former is helpful in free software tech stacks.

Five Steps to Starting a Successful Career as a Data Engineer

First step: Earn a bachelor’s degree as the first step. The finest majors are software development, computer engineering, and information systems. Alternative options are connected to engineering because this position demands more technology than science or math. No matter what your degree, make sure to bring classes in database administration, information architecture, programming skills, software design, and information structures.

Second step: Acquire Work Experience at Entry Level Seeking out IT assistant roles at your institution or a small business is a simple method to break into the field of Data Engineering. Strong proficiency in various coding technologies will be required for your job, so hone your computer and software development abilities. As you acquire experience, start creating end-to-end systems and selecting open data sets to address problems in the real world. To demonstrate to potential employers that you do have all the technical skills and persistence essential to become a Data Engineer, you will need to have this experience.

Third step: Get your debut position as a Data Engineer in the third step. Data Engineers are being hired by businesses all around the world to build their data architecture. Look for jobs with software businesses, computer manufacturers, and firms that build computer systems. This will enable you to work on projects at the forefront of data science and receive exceptional mentoring and assistance. It should come as no surprise that Silicon Valley does have one of the greatest concentrations of positions for Data Engineers nationwide.

Fourth step: Obtain relevant professional certifications such as an AI certificate. Data Engineers can get a variety of industry credentials. The PG Certificate Program in Data Science and Machine Learning, a comprehensive course that offers knowledge and hands-on experience with emerging technologies, is one well-known and well-liked choice provided by Jigsaw Academy.

Fifth step: It is to aim for higher education. You could also choose to obtain a master’s degree in computer technology or computer science as your career develops. Since Data Engineering is less academically oriented than data science, many Data Engineers excel despite having just great design and programming abilities. In most cases, a Ph.D. is not necessary for a career in Data Engineering.

What Tools Do Data Engineers Employ?

Data Engineers utilize a variety of tools; none of them is universal. Instead, every organization will make use of the technologies that it requires. Nevertheless, a few of the common tools employed by Data Engineers are listed below. Although you are not required to become an expert in every tool presented here, we advise you to grasp each key tool’s basics.

  1. Databases: SQL stays at the center of it all and is a fundamental tool for Data Engineers in our fast-paced environment where tools and technology are continuously changing. Relational database management systems are often created and managed using the common computer language, SQL. NoSQL databases are non-tabular, so they can be either a network or a record based on their data structure. Relational database management systems, Postgres, and Luna are a few common SQL databases. Numerous NoSQL databases are used today, including MongoDB, Cassandra, and Ruby.
  1. Processing data: Business organizations understand how crucial real-time data analysis is to improve business choices. Therefore, the task of creating actual data transmission and analyzing pipelines falls to Data Engineers. Apache Kafka is a well-liked tool for creating a broadcasting pipeline and is used by over 80% of Fortune 500 firms. Apache Spark is indeed an analytical engine for real-time data integration.
    For instance, Netflix processes around 500 billion events daily using Kafka, from error reports to the user watching activity.
  2. Programming dialects: To develop software and services for data difficulties, Data Engineers are often proficient in at most one computer language. Python is the most well-liked and often used computer language in Data Engineering. It has a straightforward structure, is simple to learn, and has a ton of third-party packages tailored to data needs.
  3. Integration and movement of data: As more businesses turn to cloud-based technology to fulfill their needs, transferring mission-critical apps can present several difficulties, the most challenging of which is frequently migrating the underlying database. The methods involved in moving data from one place or platform to another without affecting its security are called data migrations and integration. The act of gathering data from diverse sources and merging it productively and understandably is known as data integration.
    Data Engineers frequently utilize the real information parts of the program Striim for data migration as well as data integration since it offers up-to-date, dependable data transfer between cloud services.
  4. Decentralized Systems: Due to the vast volume of data, a single computer or system fulfills computational and storage requirements. Systems that collaborate to accomplish a shared objective yet seem like a unified system to the customer are known as distributed systems. For storing and processing massive volumes of data utilizing a computer network, Hadoop is a well-liked Data Engineering platform.
  5. Data Science And Artificial Intelligence: Because it helps them better grasp the demands of data analysts and other data users, Data Engineers must have a fundamental knowledge of the most common data science technologies. PyTorch is an accessible machine learning framework using GPUs and CPUs for data-intensive applications. Teams may construct and distribute apps driven by machine learning using the free, open-source Tensor framework.

Indian Payscale for Data Engineers

A Data Engineer’s wage structure might range from INR 3,50,000 to INR 21,00,000 each year. The need for a Data Engineer has increased as a result of the considerable growth in data compared to prior years. As a result, a Data Engineer’s pay for a newcomer in India can be a decent place to start. It should be emphasized that a Data Engineer’s pay is influenced by a variety of factors, including geography, abilities, and experience.

1. Data Engineer Payscale: Using Experience

The pay of a Data Engineer is ultimately determined by experience. Experience in a sector demonstrates greater in-depth knowledge, usable solutions, flexible working, and leadership abilities to mentor others.

2. Data Engineer Salary in India: Location-Based

If a Data Engineer is employed in Mumbai, their compensation may differ from those employed in Bangalore. This is so because one of the things that affect the compensation of a Data Engineer’s employment is the region. Every location or state has a unique living cost and necessity for AI professionals, and the compensation is set following those factors.

  • In Bangalore, a Data Engineer makes on average INR 931,331.
  • In New Delhi, a Data Engineer typically makes INR 902,317 a year.
  • In Mumbai, a Data Engineer typically earns a basic salary of INR 729,561.
  • In Chennai, a Data Engineer makes an average income of INR 817,425.

3. AI engineer Salary: Based on Skills

A Data Engineer should focus on learning the many abilities needed for a Data Engineer to land a high-paying position with little expertise. They have to consider upgrading their skills beyond merely getting a certificate in data analysis.

Main Motives for Becoming a Data Engineer

Examine a few of the factors that make a profession as a Data Engineer relevant in the modern world.

  • High Demand: Data is there wherever we look. Additionally, such a large volume of data is urgently needed since every query has a unique solution that reflects the extent of data. Data Engineers are consequently in high demand right now.
  • The ability to cope with difficulties: A position as demanding as a Data Engineer necessitates everyday problems like developing new tactics, solutions, and data configurations. This improves your ability to combat problems with sensible solutions rather than merely sitting in front of the computer worried.
  • Growth is apparent: In this sector, you are always learning new things due to the daily assessment of your persistence, problem-solving abilities, and analytic and visual capabilities. Additionally, there is a ton of room for advancement in this role through the study of increasingly difficult languages and database architectures.
  • You’ll be paid more in the long run: The pay of a Data Engineer may not be quite as high as that of other positions in specific industries. However, your salary may increase depending on your degree of expertise and skill development. One can continue and operate in this profession more and more as a result of the possibility for growth.


Data Engineering realism is flourishing and will continue to address profitable prospects in future generations. On either hand, it makes it more important to reskill and upskill to become job-ready. The projected increase as you go from a fresher to an experienced status would be around 30% and 35%; however, as you acquire experience, this number would grow dramatically. Before working as a Data Engineering professional, you must enroll in a course that will assist you in developing the necessary abilities. Check out the PG Certificate Program in Data Science and Machine Learning by UNext Jigsaw, with the most robust curriculum and hands-on learning intensive pedagogy.

Related Articles

Please wait while your application is being created.
Request Callback