A specialist workforce, known as data engineers, works on data-centric software solutions where they create them effectively to deliver an optimized solution following company expectations. Data engineering is a discipline of data science, where the aspiring candidates work in the processing of the collected data to form a fault-tolerant architecture that will be used for the transformation and transportation of the processed data within the organization.
To make the raw data more accessible and useful to the required teams within the business, they develop pipeline-like structures that are used for the transportation and transformation of the data.
Brace yourself, now it’s the time to look at the points that we will be covering in the blog, and let’s get started.
Table of Content
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What is meant by Data Engineer?
In crisp words, data engineers refer to those specialized individuals who work in the field of data science where their sole purpose is to process unstructured data into readable, more accessible information.
- The processed raw data is used to create highly optimized software that works on the algorithms prepared by the data engineers.
- They gather complex data, process them, and manage them in a stored place called a data warehouse where data is transferred through a strong architecture of pipelines.
- While working on the transformation process, they also manage the data and extract useful information from it.
- That extracted information can be used by the organization to make a plan of action for the future to increase the yield percentage.
- This whole process, carried out by the data engineers, gives detailed insights into the collected data, which can be helpful for better yields and deciphering the mood of the market.
Moving deep into the topic, let’s throw some light on why one should choose data engineering as a full-time career.
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Why pursue Data Engineering?
There are several occupations available. Why do you want to work in data engineering?
What else makes data engineering the most lucrative and in-demand position in the entire tech stack, and why should someone choose it as a full-time career path? You can answer these questions with our assistance. Continue reading.
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Look at the following details for solutions:
- Technology is driven by data, organizations use a variety of technologies, and data is the primary resource for everything.
- Data is now an asset for the firm as a result.
- We are advancing in technology but we lack skilled manpower. This is called data illiteracy, and because of this shortage, the demand for data engineers is increasing.
- Data engineering is the backbone of data science, and someone who wants to have a good career in data science should pursue data engineering.
- Aspirants can bag different technologies in their portfolio with good exposure to other types of technologies.
Here we conclude the reasons why you should opt for Data Engineering as a full-time career. In the next section, we will check out what it takes to be a data engineer.
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What is needed to be a data engineer?
The following abilities are necessary for one to be a data literate engineer in the field of data science:
- The first is having an engineering background because almost all businesses favor techies over non-techies.
- An adequate understanding of coding and problem-solving. Languages like Python, R, Ruby, Pearl, and others are preferred.
- Mastering the needed data engineering tools for the implementation of the learned techniques.
- Having hands-on experience, that is a portfolio or a real-life project by your side.
- Having a basic knowledge of statistics and a good grip on computer fundamentals.
- An aspirant with good analytical and critical thinking skills who can take the right decisions after data evaluation.
- If a person has basic knowledge of data science and associated technologies, it will act as a cherry on top of his career.
Now we will enter into the heart of the blog, the upcoming section will let you know about the general salary insights moreover salary differences over multiple parameters.
To get an in-depth description of this job, then go through the Data Engineer Job Description blog.
Salary Insights of Data Engineering
Data engineering is the most demanded domain in the field of data science and associated technologies.
- Therefore, the above-mentioned skills are required by a data literate data engineer.
- After western nations, India is the highest one to recruit data engineers as full-timers.
- The aspiring data engineer should have a strong grip on computer programming.
Look at the below parameters on which the salary of a data engineer is governed in India:
Check out our blog on the Top Data Engineer Interview Questions and Answers to enhance your knowledge in the field of Data Engineering.
Wage parameters in Data Engineering in India
Below are mentioned the parameters by which the salary of a data engineer is governed:
- They should have the above-mentioned skills by their side and should possess the above intermediary skills.
- Parameters like company size, company reputation, and geographical location also play a major role in deciding the salary of an individual.
- The experience of the candidates affects the pay and it may vary with high differences in context with freshers.
- Level of skill set the applied candidate is having.
Salary expectations in India
Considering the above-mentioned pointers, according to renowned online sources, namely Glassdoor and AmbitionBox, the average annual salary of data engineers at the entry-level is approximately ₹8 LPA, and with experience, the figures can be up to ₹20 LPA. Let’s further take a look over the salary based on different parameters.
- Salary based on experience –
- Experience plays a major role at the time of hiring and salary negotiations, the experience of the candidates tells the recruiter about their proficiency, contribution to the community, and knowledge of industrial working culture.
- Greater the experience, more the salary will be. Company-switching candidates can expect a bigger hike.
- The below-mentioned table depicts the salary insights over the experience.
|Working Period||Expected Average Salary|
|Spanning less than 1 yr.||₹ 4.5 LPA|
|Spanning between 1 yr. to 5 yrs.||₹ 7 LPA|
|Spanning between 6 yrs. To 9 yrs.||₹ 12 LPA|
|Spanning over 20+ yrs.||₹ 18 LPA|
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- Salary based on Geolocation
- Another factor that affects the salary of candidates is geolocation, which means where the employer is, where the company is situated, and where the candidate applying from affects the salary for the same.
- IT hubs like Bangalore, Pune, and Mumbai offer higher pay packages as compared to non-IT hubs like Kolkata and Jaipur.
- Let’s have a look at what stats have to say so that you can have a clearer idea.
|Location||Average Salary Offered|
|Bangalore||₹ 9.5 LPA|
|Mumbai||₹ 7.1 LPA|
|Pune||₹ 8.2 LPA|
|Ahemdabad||₹ 4.5 LPA|
|Kolkata||₹ 5.1 LPA|
|Jaipur||₹ 5 LPA|
- Salary based on Employers
- Name of the employer changes the game of average salary expectations if you are applying to companies like Google, Amazon, Deloitte, Accenture they pay better salary as compared to companies like Cognizant, Capgemini, TCS.
- Better the company, better the company structure, better the company pay packages.
- Look at the below-mentioned table for the salary comparisons.
|Employer Name||Average Salary Paid|
|Amazon||₹ 20+ LPA|
|Accenture||₹ 6.2 LPA|
|Deloitte||₹ 13 LPA|
|Cognizant||₹ 7.6 LPA|
|Tata Consultancy Services||₹ 7.1 LPA|
|IBM India||₹ 8.1 LPA|
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- Salary based on Skill Set
- Skill plays an important role in deciding the future of Data engineers in the IT industry.
- Certain skills like SQL, Hadoop, and Apache Spark are required by the data engineers to stand alone in the market even as a fresher.
- Check out the table to see which skill is the most demanded and offers greater bucks.
|Skills||Average Salary Expectations|
|SQL||₹ 8.2 LPA|
|Programming Languages (Python, R, Ruby, Pearl, Java)||₹ 8 LPA|
|Apache Spark||₹ 9.5 LPA|
|Data Warehousing||₹ 9 LPA|
|Hadoop||₹ 8.4 LPA|
|ETL (Extract, Transfer, Load)||₹ 8.5 LPA|
India has the second highest market for data engineers as companies are data-dependent and willing to drop huge funds while hiring them. The figures for job availability and salaries are competitive. An aspiring individual can take a lot out of this career as it will only increase exponentially in the future.
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