- What is Data Engineering?
- Who is a Data Engineer?
- What is the Demand for a Data Engineer?
- Data Engineer Salary
- Data Engineer Salary Deciding Factors
- 1. Database Engineer Salary: Based On Experience
- 2. Database Engineer Salary: Based on Location
- 3. Database Engineer Salary: Based on Skill Set
- 4. Database Engineer Salary: Based on the Employer
- Data Engineer Salary in Other Countries
- Data Engineer Job Roles and Responsibilities
- Data Engineer Job Roles
- Data Engineer Responsibilities
- Data Engineer Requirements
- How to Become a Data Engineer?
- Step 1: Getting the Right Education
- Step 2: Acquire the Skills Required
- Step 3: Build Your Portfolio
- Step 4: Advance Professionally
What is Data Engineering?
Data Engineering is the science of developing and creating systems for data collection and analysis.
As the name suggests, Data Engineering is the combination of data management and engineering. It is a mechanism designed to build effective, smooth, and efficient information systems for leveraging data for strategic and insightful purposes.
Who is a Data Engineer?
A Data Engineer is a technical professional who is responsible for creating an architecture that caters to the needs of data scientists in procuring, storing, and transforming raw data for analysis and interpretation.
The data engineer can build algorithms and pipelines that turn a large amount of data into vital information for businesses.
To explain this in more detail, suppose in a company a data analyst or a scientist will work on data sets to make strategic decisions. But a data engineer will be employed to create a pipeline for extracting data from different sources, building algorithms to consolidate and cleanse the data.
Companies have a huge set of data to store, process, and analyze. Data engineers are the experts who ease down this process and bridge the gap between scalable pipelines for gathering and storing data to analyzing and interpreting it.
What is the Demand for a Data Engineer?
It is apparent that in present times every organization is mining data for growth and development purposes. Not only to make strategic decisions, but companies are using data for heavy research-oriented campaigns and policies.
A data-driven approach is what industries are heading towards for finding what their customers like and how well they are doing out there in the market.
For this, there is a surging demand for Data Engineers. According to a recent survey, there has been an influential increase in demand for data engineering job positions.
Also, according to the Dice 2020 Tech Job Report, there is a 50 per cent YOY growth in demand for data engineers in India.
So, let us look at what job roles and responsibilities this in-demand job position requires.
Data Engineer Salary
The average Data engineer salary in India is ₹836,443 per annum.
It can range from the minimum of ₹368k per annum to the maximum of ₹2 million per annum.
Data engineers are quite in demand by many organizations and businesses and therefore, even during the pandemic have a significantly good pay base for their position.
However, there are certain factors that affect the average pay base for a data engineer. Those factors can be experience, company, job role, location, skillset, etc.
Let us read about in detail how different factors decide the average salary of a data engineer in India.
Data Engineer Salary Deciding Factors
1. Database Engineer Salary: Based On Experience
Experience matters a lot in deciding the average data engineer salary in India. Experience tells a lot about your personality, contributions in the field, and knowledge about the industry and job profile.
Therefore, experience is directly proportional to the better pay structure.
Higher the number of working years and extensive experience portfolio, the better the average salary for a data engineer.
|Average Base Salary/annum (In INR)
|Fresher (>1 year)
|Early Career (1-4 years)
|Mid Career (5-9 years)
|Experienced (20+ years)
Look at the graph below to understand how the experience plays into account in deciding the average data engineer salary.
2. Database Engineer Salary: Based on Location
Location is another vital factor affecting the average Data Engineer’s Salary. Some locations have better job opportunities, more demand for candidates, and, naturally, becomes a hub of thriving possibilities.
For example, cities like Bangalore, Pune, etc, are known for their IT sector and have so many opportunities for data engineers whereas, there is not much conversation in cities like Ahmedabad or Jaipur.
Therefore, some locations have better average salaries to offer to data engineers than other cities.
Read below to see what locations have better salary opportunities for a Data Engineer.
|Location or Cities
|Average Base Salary/annum (In INR)
|Chennai, Tamil Nadu
|Hyderabad, Andhra Pradesh
|New Delhi, Delhi
|Kolkata, West Bengal
Note below that cities like Hyderabad, Gurgaon, Bangalore have a higher average salary range than cities like Kolkata, Jaipur, and Ahmedabad.
Data Engineer Salary in Gurgaon
Data Engineer Salary in Hyderabad
Data Engineer Salary in Jaipur, Rajasthan
Data Engineer Salary in Ahmedabad
3. Database Engineer Salary: Based on Skill Set
Skills are essential to the role of a data engineer. However, some skills are more important than others and therefore, affect the average data engineer salary in India.
Suppose, it is evident that SQL would be more important for a Data Engineer than learning about Microsoft Word.
So to stand out and get a good pay base even as a fresher, certain skills should be acquired.
Find out below which skills have a better pay structure for a data engineer.
|Average Base Pay/annum (In INR)
|Programming Language (Python)
|ETL (Extract, Transfer, Load) Skills
|Data Warehousing Skills
4. Database Engineer Salary: Based on the Employer
Another significant factor that decides the average data engineer’s salary is the company or the employer. Better companies have better job positions and pay structures for data engineers.
The freshers can also get a good pay base at top-tier companies.
Some of the Top Companies that hire Data Engineers in India are:
- Amazon Inc, India
- HCL Technologies Ltd.
- IBM India Pvt. Ltd.
- Tata Consultancy Service Ltd.
Now, let us look at the table below to see the different average base salaries of a data engineer for these top companies in India
|Average Base Salary/annum (in INR)
|HCL Technologies Limited
|IBM India Pvt. Ltd.
|Tata Consultancy Services
Source – PayScale
Data Engineer Salary in Other Countries
Let us look at the average data engineer salary in other countries.
|Average Base Salary/year
|United States of America
Data Engineer Job Roles and Responsibilities
In order to fully understand what a data engineer does, you need to understand the functions of the data architecture.
The three most important functions of data architecture are:
1. Extracting: Extracting the data means gathering data from different sources. Sources can vary from a single record on the database to the feedback responses from customers digitally. Gathering information from which location is a vital function.
2. Data Storing: Data storing or data transition is as the name suggests managing and storing the data we gather. Data Warehouses act as a repository for the ultimate storage of data. It is not always about the quantity but also technical capabilities like analyzing, reporting, or the type of data models it has.
3. Transformation: Raw data is not consumable. The end-users need data that can be interpreted for analysis and decision-making. Data Transformation is a function that focuses on cleaning the data to make it consumable for the end-users for interpretation and analysis.
Data Engineer Job Roles
Now based on these functions, Data engineer Role is categorized into three areas.
The role of a data engineer generalist is a great responsibility for beginners or the ones switching to data engineering.
This job profile requires a professional who can handle everything data-driven. This could include working on gathering data, managing it, analyzing it, and learning basic steps into the technicalities of engineering a system for data analysis.
This job profile is usually in-demand amongst small companies who are looking for professionals good with engineering as well as data analysis skills.
Data engineers have the responsibility of working with databases for creating storage. The warehouse-centric role concentrates the data engineers’ expertise in working with data warehouses. They work on developing tools and structures to manage big data and connect it with different types of databases.
Data Pipeline engineers have the responsibility to create an ecosystem that manages the flow of work from data sources to data warehouses. They work with data scientists in developing pipelines and maintaining data integration tools for specific or bigger tasks like data storage points prior to transformation or known as the data staging area and so much more.
Data Engineer Responsibilities
The roles discussed above were specific but on a broader term, there are responsibilities expected from a data engineer.
Those data engineer responsibilities are as follows:
- Develop an architectural design for data platforms
- Manage and enhance the existing data structures and pipelines for efficiency
- Customize and develop data integration tools for smooth workflow
- Interpret data trends for business objectives and goals
- Structure and manage data and metadata (data that describes about the other data) via database systems
- Ensure the stability and performance of data pipelines
- Set up new and updated tools for data extraction and analysis
- Build efficient algorithms for analysis and interpretation
- Evaluate business needs and requirements
Data Engineer Requirements
- Bachelor’s or Masters degree in Computer science, Technology or any other related field
- Knowledge of Technical skills like Apache Spark, Apache Hadoop, ETL tools, etc.
- Proficiency in programming languages like C++, SQL, Python, etc.
- Basic understanding of operating systems like Linux, UNIX, Solaris, etc.
- Project-Based Learning
- Prior experience is preferred.
- Clear fundamentals of Data structures, Data Warehousing solutions and Data modelling.
- Requires soft skills like communication, collaboration and problem-solving.
How to Become a Data Engineer?
Data Engineering is a flourishing field where not only data-driven companies instead every industry, and every business demands a data engineer.
Now, if you are just starting as a data engineer or at a point in your career thinking of switching fields, you might need the right path to become a successful data engineer.
Here is a comprehensive path to becoming a data engineer.
Step 1: Getting the Right Education
Education here can encompass a background in maths, statistics, analysis or a professional degree.
Now, many companies prefer a bachelor’s degree in computer science, data analysis or any other related field. Some companies might not.
But getting the right education is not just about building a resume, it’s about gaining fundamental knowledge of the field. It can give you a taste of broader terms, an open perspective on the domain of data engineering and other related fields.
So, the right education, a degree, or a professional course is not compulsory but preferred.
Step 2: Acquire the Skills Required
Here are some skills required to become a great data engineer.
- Proficiency in Programming Languages
It is important to understand that data engineering is a perfect mix of data analysis and software engineering.
A data engineer unlike, a data analyst, is handling the technical tools, and data infrastructure for managing and storing the big data. It requires proficiency in languages like Python, Java, Scala, etc for building data models, frameworks for the architecture and so much more.
Therefore, learning about programming can help you stand out from the crowd.
- Understand Automation and Scripting
Getting to know the fundamentals of automation and scripting is important for a data engineer to ease down your workflow and increase speed. Automation can help you automate some repetitive tasks like cleaning up the database, checking for new updates hourly, etc that can be tedious when done manually.
Shell Scripting, Cron, are some of the methods and tools for scripting and automation.
- Master the Databases
Databases are a vital aspect of the data engineering field. You need to know just about the databases but everything around them.
Learn about different database systems whether relational database systems or No-SQL databases. Proper knowledge of such databases allows you to work efficiently with structured or loosely structured, semi-structured data.
Master the SQL (structured query language) that helps you in working with relational database systems.
Also, learn the basics of data modeling. It is a mechanism of how the system interacts with different entities in the database. Learning about techniques like star schema, data normalization, etc.
- Proficiency with Data Processing techniques and solutions
Data engineers in big organizations work with large data sets and should be proficient in data processing techniques and tools like Spark Streaming, Apache Hadoop, and so many more.
In addition to this, proficiency in data warehousing solutions is also a must. A data engineer will require to learn about cloud computing, Amazon Web Services, for example, to manage the data sourcing from different sources.
- Knowledge of Data Structures and Algorithms
DSA (Data structures and algorithms) increases efficiency and speed while working on complex code for data architecture and data modeling.
A data engineer will understand the data from the organization’s perspective as a whole and find the right solution for it. It also is essential to highlight their problem-solving skills in times of crisis or emergency.
- Soft Skills
A data engineer to stand out from the crowd does not always require technical skills and methods. A data engineer should have proficient soft skills as well. Some of the data engineer soft skills required are:
- Presentation Skills
- Time Management
- Team Work
- Attention to Detail
Step 3: Build Your Portfolio
After all the skills and knowledge you acquire, you cannot just sit and wait for the dream company to just fall on your lap. To reach there, you would want to start working on small projects, and in small startups to gain experience and learn working.
It is the step where you take up freelance projects or work as an intern for different organizations to build your work portfolio, gain real-life exposure to the field and find your speciality.
Step 4: Advance Professionally
Never stop upskilling yourself. Even after a successful job at a big firm, advance professionally for your learning curve.
Take up on your skills that need mastery, or any other new skill required to become the best data engineer out there.
Keep learning and growing.
This roadmap is not objective but a perfect guide for you to start off as a data engineer.
If you are passionate about software engineering and data analysis, data engineering is what you can explore.
With the combination of analysis and programming, Data Engineering is a discipline that is in huge demand by top-tier industries and companies.
There are not single but multiple reasons to become a data engineer in today’s time.
From better data engineer salaries in India to thriving job opportunities, the data engineer job role has it all.
Start learning skills required to become a data engineer today if you want to explore this flourishing field.
Q.1: Do Data Engineers code?
Data Engineers do not code extensively like a programmer. However, they need proficiency in programming languages for tasks like building data infrastructure and developing data models. For example, managing databases requires SQL, and to be adept with the DSA and machine learning programming is needed. So, the basics of Python, SQL, and Java will help them stand out.
Q.2. Do data engineers use Python?
Yes, Data engineers use Python while designing and developing data architecture for the data scientist.
Q.3: Is Data Engineering in Demand?
Yes, Data engineers are quite in-demand by top-tier companies like Amazon, Deloitte, HCL, TCS, etc.