State of Data Engineering in India 2022

State of Data Engineering in India 2024

Data Science is a wide discipline with many smaller domains under its umbrella. Many elements such as analytics, machine learning, business, and software engineering work together to create the data science ecosystem.

One branch critical to the data science job revolves primarily inside the software engineering domain and is popularly known as Data Engineering.

The primary responsibility of a Data Engineer is to prepare data for analytical or operation purposes. These software engineers are often in charge of creating data pipelines to collect information from different source systems. 

They integrate, consolidate and cleanse collected data, and structure it for use by data scientists and analysts. Data engineers aim to make real-time data access and optimize their organization’s data ecosystem.

With the global big data and data engineering market expected to grow at a compounded annual growth rate (CAGR) of around 18% from 2021 to 2027, let’s take a holistic view of the current state and the scope of data engineering in India.

The current state of data engineering in India

As the demand for collecting, storing and analyzing information increases, the role of a Data Engineer is gaining prominence in the tech ecosystem. According to industry reports, data engineers are among today’s top three analytical roles in the Indian market.

Below is an overview of job openings, median salary, attrition rate, and prevalent skills in India’s current data engineering market.


With Data Science and Machine Learning surging exponentially, data engineering provides the foundation for many industrial use cases. The market for data engineering in India is experiencing an all-time high demand, which will only grow with time.

Let’s review some of the prominent highlights representing the data engineering spectrum.

  • The volume of the Data Engineering market in India stands at a whopping USD 18.2 billion as of 2022. Moreover, this number is expected to rise at a CAGR of 36.7% within the next five years and is projected to reach USD 86.9 billion by 2027.
  • The Banking and Insurance sector employed the highest share of data engineers, constituting around 37.7% of the share of all non-IT sectors.
  • Over 30.8% of the employable data engineers hosted a work experience of 3 to 6 years.

Jobs and demand

It is no secret that the need and demand for skilled and intuitive data engineers are reaching a fever pitch in the IT sector. The Dice 2020 Job Report noted that data engineering is the fastest-growing career option in technology in 2019, which marked a 50 percent year-on-year growth in the number of open positions.

  • The total number of vacant Data Engineer positions waiting to be filled as of May 2022 stands at over 36,000.
  • For over 10,000 jobs in the data engineering markets, skilled professionals can demand a salary of INR 10-15 lakhs per annum.
  • Over 40% of the listed Data Engineer positions are suitable for professionals with 5 to 10 years of experience. 


There is a common theory that positions and inflated salary packages for data engineers are abundant but a glaring lack of skilled professionals. The resulting shortage of industry-relevant skills has made firms desperate to pay lucrative compensation packages to mid-level experienced data engineers and skilled freshers. 

  • The median salary on display for data engineers hovers around the range of INR 17 lakhs per annum.
  • When it comes to data engineers employed in the Internet or E-Commerce industry, they can easily command a high median salary of around INR 28.5 lakhs per annum.
  • Data Engineers working out of the Delhi NCR region pull the highest median salary of around INR 19.3 lakhs per annum, closely followed by professionals from Bengaluru who command around INR 19.0 lakhs per annum.

The demand for data engineers couldn’t be dampened even by a worldwide pandemic. Unlike the norm during disastrous conditions, such positions saw increased hiring and base pay across multiple organizations.

However, it is important to note that pivotal factors like experience, company, job role, location, skillset, etc., play a huge role in determining the average base pay plus perks and benefits for an employable data engineer.


Attrition rate points to the churning of individuals on their way out of the company. The reasons can be voluntary or involuntary, including termination, resignation, retirement, etc.

  • The overall attrition rate of data engineering in India stands at approximately 34%.
  • Professionals with 0-2 years of experience in data engineering technologies face the highest attrition rate of over 41%.
  • It is important to note that the attrition rate for boutique analytics firms stands at almost twice the attrition rate of IT services, which tends to be the lowest across company domains.

Key skills

Data engineering in India demands certain technical know-how that can be acquired through various avenues.

  • Python is one of the more intuitive and well-known languages that data engineers use as a tool. More than 9 of 10 professionals inclined to learn data engineering start with Python.
  • With big Data Engineerskills in high demand, around 40% of professionals now host the ability to work in cloud environments hosted by Microsoft Azure, which is the most among other technologies.
  • For data engineers with around 0-3 years of experience, SQL is the most popular programming language for database queries. 


With more and more professionals trying to learn data engineering for a career switch or growth, the rise of data engineering in India is beginning to take shape.

According to a management consulting firm named Zinnov, the data engineering market share is projected to increase around four times to over USD 42 billion by 2025. This is a huge leap from the current market share of around USD 10 billion.

The challenges of data engineering

With the growing need to handle and process huge amounts of data, Big Data Engineer skills are becoming a must-have. Not long ago, even the thought of storing and manipulating large-scale data warehouses would send chills down the companies’ spines.

But some functional challenges remain on the road to managing such vast amounts of rapidly growing data. Let’s understand some of these bottlenecks in brief.


It is common knowledge that with the growing volume and variety of data, organizing it into handleable parts is quite a tall task. This is when a need for an added layer of information, conceptually known as data about the data or metadata, arises.

There are multiple prominent pieces of information like data sources, time of updates, description of schema, and other useful tidbits. These act as guides for large data pools and help navigate through them.

Data security 

Since data is increasing exponentially, security is bound to be compromised in the long run. The variety of data coming from different sources in different pipelines makes it susceptible to hackers’ attacks, leading to sensitive information being leaked. 

This acts as one of the roadblocks to data engineers getting access to use the data due to the vulnerability that clouds it. Additional security touchpoints and leveraging cloud platform security protocols are the main ways to ensure the security and integrity of data. 

Machine Learning and predictive analytics have often been leveraged to track and prevent attacks.

Multiple data sources

Real-time data is pouring in at lightning-fast speeds and in massive volumes. Handling this data from different software and platforms and bringing it to a common standard that can be used for further processing is a bigger challenge than it seems. 

Virtual data warehouses have popped onto the scene with their ability to connect data from different locations and consolidate it in a dedicated cloud-based repository. Such methodical storage leads to actionable insights from the data that can be credibly deployed to solve business problems.

Data quality

Data in its truest form is messy and hard to understand. It has to go through an entire cleaning pipeline before it can be processed and fed to machine learning models.

Poorly curated data is a huge concern in data science and data engineering, as it affects the foundation of all other activities to follow.

Top data engineering skills in demand

The skills required to become a proficient Data Engineer are not only comprehensive know-how of the tech stack but also an intrinsic intuition to play with data and draw meaningful insights. 

Let’s take a look at some of the prominent data engineering skills.

  • Comfort in coding and handling data using Python, Java, C++, Scala, R, etc.
  • Proficiency in analytics to work with large-scale unstructured datasets.
  • Adequate understanding of query languages like SQL, along with precise know-how of relational databases
  • Ability to build big data pipelines and design architectures.
  • Ability to perform root cause analysis of internal/external data and processes to build solutions from the smallest subparts and identify the scope of improvement.
  • Hands-on knowledge of big data frameworks like Hadoop, Kafka, Flume, Hive, etc.

Emerging technologies in data engineering

With the advent of social media and the increase in mobile devices, there is a noticeable shift from batch-oriented data to real-time data. This, in turn, requires real-time data pipelines and real-time data processing systems.

Understandably, data warehouses house immaculate flexibility to store data marts, data lakes, and simple use case datasets and have become well-known as of late.

 Let’s further understand how streaming technology enables cutting-edge business analytics at scale and other technological shifts that will rule data engineering’s future.

  • Ever-increasing connectivity between the source of data and data warehouse storage.
  • Data engineering automation avenues enable self-service analytics from smart devices on the go.
  • The batch ETL quickly pivots to handle the modern real-time data flow model. Even the legacy ETL jobs are being reprogrammed to execute in real-time in the current data engineering systems.
  • The ability to choose between on-premise, cloud environments, or a midway hybrid data architecture.

The future of data engineering 

It is no rocket science that the future of data engineering is being built on the cloud. Not only does this transition to cloud-based systems enable the handling of real-time bulk data, but it also facilitates industry-specific automation.

Even though the tools to carry out data engineering tasks may get refined over time and undergo updates, the value and intuition of a Data Engineer to make sense of data will never go obsolete. 

In the bigger picture, the data boom is still nascent. And acquiring data engineering skills can put professionals at the cusp of a revolutionary time in tech.

The amount of data an engineer has to deal with varies on the organization’s size. The bigger the company, the more intricate the analytics architecture, and the more data an engineer will be responsible for. Certain industries such as Healthcare, Retail, and Finance Services are a few examples of top data-intensive industries. 

Data engineers work with the data science team to improve data transparency and enable top management to make more trustworthy business decisions.

The global demand for data engineering is particularly on a sharply rising trend. The main driver behind this trend is the rapid increase in the volume of unstructured data due to phenomenal growth in interconnected devices and social networks.

The Hero Vired Certificate Program in Data Engineering is a great step if you want to find a way into the field of data engineering with a comprehensive learning program

With 70-90% live online instructor-led classes, the program is designed to train candidates on how to extract, transform efficiently, and load data into consumable and usable information for business analysis. 

The industry-validated curriculum ensures students learn to use the latest industry-acclaimed technology stack to engineer data and solve relevant business problems.






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