Big Data Salaries Guide 2022 – Who Earns How Much

Did you know that at present, India is one among the top 10 big data analytics markets in the world? According to a report titled “Big Data Analytics Market – Future Scope in India,” currently the Big Data Analytics market in India stands at $2 billion. It is anticipated to grow at a CAGR of 26% by 2025 (reaching approximately $16 billion), thereby making India a 32% shareholder of the global Big Data Analytics market.

What then, can we infer from this?

The answer is simple – the Big Data job market in India is also growing by leaps and bounds. The Quant Crunch Report maintains that by 2020, there will be over 2.7 million big data and analytics jobs open to qualified individuals, which is nearly 400,000 more job positions than now!

But there’s a catch – while the demand for Big Data professionals is increasing by the day, their supply is far from enough to fulfil all the job vacancies. In fact, out of the two-thirds of the Big Data skill requirement, only one-third are met.

Owing to the demand-supply paradox, most job positions in Big Data pay handsome salary packages. Almost all the job roles in this field require both advanced degrees and a certain level of expertise. And in the present scenario, this combination is pretty challenging to find. This is precisely the reason why companies are ready to pay high-end salaries to candidates with the right skill set. Most Big Data job roles compensate in the six-figure range and well above the average market pay scale.

Before we dive into Big Data salaries, let’s look at the top ten high paying job positions in Big Data.

  1. Big Data Engineer

Big Data Engineers process and analyze vast volumes of data and transform it into meaningful business insights that can benefit organizations in myriad ways. They also retrieve a company’s data gathered from multiple sources and analyze, interpret, and report on the same for smarter business decisions.

The average salary in India: Rs. 7,24,280

The average salary in the US: $ 1,16,590

  1. Data Architect

Data Architects focus on designing, structuring, and maintaining the data within relational databases. Also, they develop actionable strategies for enterprise data models as well as communicate project plans and their status to the Managerial Executives of the company.

The average salary in India: Rs. 17,32,690

The average salary in the US: $ 1,23,680

  1. Data Warehouse Manager

Data Warehouse Managers are tasked with the responsibility for storage and analyzing data in data warehouses. They use a combination of performance and usage metrics to evaluate data, monitor data usage, analyze data load, etc. They also must identify & mitigate potential risks to the data stored within warehouses from time to time.

The average salary in India: Rs. 5,90,000

The average salary in the US: $ 1,34,270

  1. Database Manager

Database Managers primarily deal with the diagnosis, maintenance, and repair of databases. Apart from this, they also assist Database Developers in the design and physical implementation of storage hardware.

The average salary in India: Rs. 4,90,500

The average salary in the US: $ 72, 550

  1. Business Intelligence Analyst

Business Intelligence Analysts possess a strong analytical background, and they aid organizations in transforming their raw data into valuable business information that can be further used in strategic decision-making. They not only scrutinize and analyze massive datasets to uncover the hidden patterns but also handle the management team’s requests for specific information.

The average salary in India: Rs. 7,07,331

The average salary in the US: $ 90,920

  1. Data Scientist

Data Scientists not only gather, process, and analyze raw data, but they also design and construct new methods for data mining, data modeling, predictive modeling, custom analysis, developing algorithms, and much more. Their job demands a high level of expertise in mathematical, statistical, analytical, and ML skills.

The average salary in India: Rs. 10,10,110

The average salary in the US: $ 1,17,350

Learn more about Data Scientist Salary in India

  1. Data Modeler

Data Modelers are professionals skilled in Information Science and Statistical Analysis with a flair in programming. They are responsible for creating and implement sound data strategies, developing data flows, developing conceptual data models, etc., by using the best industry practices to ensure high data quality and minimize redundancy.

The average salary in India: Rs. 10,64,230

The average salary in the US: $ 97,920

  1. Database Developer

Database Developers are tasked with the duty of analyzing current database procedures to eliminate inefficient and redundant coding, and also modernize them. They have to perform tasks like monitoring, troubleshooting, and debugging to solve performance-related issues of databases.

The average salary in India: Rs. 6,00,000

The average salary in the US: $ 83,980

  1. Database Administrator

Database Administrators focus on the planning and development processes of databases. They constantly monitor and optimize database performance to prevent the damaging effects caused due to high traffic, accessibility, etc., and also troubleshooting database issues for lag-free performance.

The average salary in India: Rs. 5,82,440

The average salary in the US: $ 80,690

  1. Data Analyst

Data Analysts gather and store data (sales numbers, market research, logistics, linguistics, etc.) from various sources and interpret it into such information that can change businesses for the better. The perform QA tests to ensure the quality and accuracy of the data and find ways to help individual entrepreneurs, corporations, and other organizations.

The average salary in India: Rs. 4,97,540

The average salary in the US: $ 67,380

Big Data Salaries

When it comes to salary, Big Data is a highly lucrative career option in India. Freshers in the field with a Master’s degree can fetch anywhere between Rs. 4.5 – 10 LPA. As for experienced professionals, those having 6-10 years of experience can make between Rs. 7.5 – 15 LPA and those with over 15 years of experience can easily make around Rs. 20 – 30 LPA.

While it may sound surprising, but in India, Big Data salaries are about 50% more than that of IT salaries. So, if a fresher in IT earns Rs. 3.5 LPA, a Big Data fresher, would receive about Rs. 6.5 LPA. Also, the salary scale for managers and non-managers differs to a great extent – while a Big Data Manager would make around Rs. 16 LPA, a non-managerial staff in Big Data would earn close to Rs. 8.5 – 9 LPA.

If you look at the Big Data salary structure pertaining to skills, you will see that candidates with specialized skills and competencies tend to earn more than their averagely-skilled counterparts. For instance, a fresher having expertise in Hadoop architecture can make around Rs. 9 – 14 LPA, whereas a Big Data Manager with the same skill set (but with more years of experience) will make near about Rs. 15 – 18 LPA.

Both in India and the USA, big names (like Google, IBM, Fractal Analytics, Amazon, Oracle, etc.) and startups are always on the lookout for talented and skilled candidates in Big Data technologies. You should also know that when the big players in the field find the right candidates having the requisite skill set, they can hike the salary package up to 250%!

Thus, it is plain to see that a career in Big Data, be it in India or the US, is a very bright and promising one. The handsome salaries and incentives are what attracts young aspirants to choose this career path. Not just that, tech professionals in their mid-career are also upskilling to make a shift to Big Data. After all, who’d miss out on the opportunity to work with some of the biggest names in the industry while making tons of money!

If you are interested to know more about Big Data, check out our PG Diploma in Software Development Specialization in Big Data program which is designed for working professionals and provides 7+ case studies & projects, covers 14 programming languages & tools, practical hands-on workshops, more than 400 hours of rigorous learning & job placement assistance with top firms.

Check our other Software Engineering Courses at upGrad.

How is Big Data different from data?

Data refers to any entity, such as characters, symbols, numbers, etc., on which users can perform operations that can be accomplished through a computer or any other device. Electrical signals are used to store and transmit this data, which is then recorded on magnetic, optical, or mechanical media. Big Data refers to vast, diversified amounts of data that are growing at an exponential rate. It includes the volume of data generated and gathered, as well as the velocity and variety of the data points covered. Big Data is frequently derived by data mining and comes in a wide variety of forms.

What are the types of Big Data?

Big Data mainly consists of three different types of data, known as structured, unstructured, and semi-structured data. Structured data refers to data that can be recorded, retrieved, and processed in a predetermined way. Unstructured data refers to data that has an undetermined shape or organization. In addition to its enormous bulk, unstructured data presents a number of processing obstacles for extracting value from it. Both structured and unstructured data can be found in semi-structured data; where the data might look structured, it is actually not.

What are the advantages of Big Data?

The ability to process Big Data has a number of advantages, including the ability for businesses to use outside intelligence when making choices. Organizations can work on improving their business strategy by using statistics from search engines and websites. This enhances customer service. Big Data technologies can be utilized to create a platform or staging region for new data. Furthermore, combining Big Data technology with a data warehouse allows an organization to offload data that are visited infrequently. Consumer replies are studied and reviewed using Big Data and processing and machine learning technology, which aids in the early detection of product or service risk and improves operational efficiency.

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