Over the last decade, transformation and change is driven largely by technology and with access to the internet, companies, industries, economies are changing and upgrading to match the pace of the changes happening all around us. Technological changes have led to creation of a wide range of opportunities in terms of career and business opportunities. Digitalization has opened a wide array of career opportunities that include data engineers, data scientists, web developers and full stack developers are few among a whole array of choices.
The focus today is on data engineering, what it encompasses and the future of this as a career path.
The last few years have seen a massive overhaul of data which has led to the role of data engineers becoming important and vital as they provide insights, after examining, doing a deep dive, and mining data to businesses and organizations in order to make informed decisions.
Data engineers lay down the groundwork of a database and its planning. They evaluate a broad spectrum of requirements and customize this database to suit the needs of business and companies. Macro and micro factors will also be taken into consideration while developing models and prototypes.
Data engineers can also support the data science team by creating dataset measures that can help with data mining, modeling, and production. In this way, their involvement is decisive in improving the quality of data.
Comprehending and understanding data is just the last piece of the puzzle, as the data goes from its unfiltered version to the final filtered and processed form. Processing data methodically requires a steadfast network known as a data pipeline: a set of technologies that form a precise setting where data is attained, deposited, handled, and queried.
According to the recent data churned by Burning Glass’s Nova platform, Data Engineer jobs ranks as the top job in the tech domain, recording an 88.3% increase in job postings in the span of twelve months.
What does a data Engineer do?
The role of a data engineer is as adaptable as the project requires them to be. Data engineers are crucial to businesses in the changing and evolving landscape today. The more progressive the technology such as machine learning or artificial intelligence are involved, the more multifaceted data platforms become.
The principal job of a data engineer is to develop an unfailing structure for changing data into such plans that can be used by data scientists. Apart from building pipelines to covert raw data into serviceable arrangements, they must also recognize trends in big datasets. They work on all types of structured and unstructured data and then convert these data so that organizations have an advantage in their decision-making process.
Roles and responsibilities
So far, we have just covered a broad framework of what it means to be data engineer, below are the detailed overview of the roles and responsibilities.
Integrate, consolidate, and cleanse data collected from multiple sources.
Organize detailed analytics programs, machine learning algorithms, and statistical techniques to develop and launch successful data pipelines.
Develop the necessary setup for best extraction, transformation, and loading of data from incongruent sources using SQL, AWS, and other Big Data technologies.
Recognize and develop advanced ways to improve data reliability, competence, that bring and add value to businesses and organizations.
Work with peers, colleagues, and clients to understand the nuances of projects and their expectations.
Keeping the data structure and design updated and in time with latest develop
Rethink and redesign existing models to optimize their functioning.
Upscaling yourself not only with business, but also other factors that could impact the organization
Accumulate massive and intricate data sets to supply to the functional and non-functional business requirements.
Data Engineer Salary: Based on Location
Despite the remote working options, today; location and which part of the country you are working in plays an important role in salary structure of a data engineer
Location plays a vital role today and people are shifting to metropolitan cities to rake in the opportunities available to them here. Below is the list of average salary offered to a data scientist and the corresponding city, to give you a clearer picture
| City | Avg Salary (P.A) |
| Bangalore | 942,885.00 |
| Pune | 865,618.00 |
| Chennai | 817,425.00 |
| Mumbai | 724,980.00 |
| Hyderabad | 984,305.00 |
| Gurgaon | 984,305.00 |
| New Delhi | 902,317.00 |
| Kolkata | 524,000.00 |
| Ahmedabad | 450,000.00 |
As the demand for data engineers is increasing, and opportunities are opening for them, below listed are the companies in India, who are on the lookout for data engineers
- Amazon Inc, India
- Deloitte
- HCL Technologies Ltd.
- Cognizant
- IBM India Private Ltd.
- Tata Consultancy Services
- Accenture
- Capgemini
- Wipro Ltd.
- Infosys
Source: PayScale
The average pay for data engineers in these companies are:
| Companies | Average Base Pay/year (In INR) |
| Amazon Inc | ₹1,937,775 |
| Deloitte | ₹1,300,000 |
| HCL Technologies Ltd. | ₹975,000 |
| Cognizant | ₹760,353 |
| IBM India Pvt. Ltd. | ₹709,859 |
| Tata Consultancy Services | ₹700,000 |
| Accenture | ₹620,000 |
| Capgemini | ₹620,000 |
| Wipro Ltd. | ₹516,000 |
| Infosys | ₹513,000 |
To gain an upper hand for the recruitment process data engineers should not only be well versed with their jobs m they should always keep upgrading and upscaling themselves.
Learning new technologies and what is the latest development in SQL, ETL (Extract, Transfer, Load) Tools, Programming Language (Python), Apache Spark skills , Cloud Computing, Big Data Analytical Skills will ensure it gives a competitive edge over others.
SQL
SQL serves as the essential skill set for data engineers. Without mastering the SQL skill, a data engineer cannot deliver to the best of the ability and hence they should make sure they are not only well versed but keep learning new variations.
Data Warehousing
Get a grasp of building and working with a data warehouse; it is an essential skill. Data warehousing assists data engineers to combine raw and unstructured data, gathered from a variety of sources. It is then equated and measured to expand the productivity of business operations and mitigate any risks that are noticed.
Data Architecture
Data engineers must have the essential information to build intricate database systems for businesses. These databases should take in all functionalities that are data driven, are data drivers and correlated activities of the same in order to encompass the overall functioning and operations of a business.
Coding
It is important and vital to link your database and work with all types of applications including web, mobile, desktop, IoT in order to reach a larger customer base and have enough access to data to anticipate new trends and forecast the future trends. Hence it is important to learn an enterprise language like Java or C#. The prior is useful in open-source tech stacks, while the later can help you with data engineering. Though the most necessary ones are Python and R. An advanced level of Python knowledge is valuable in data-related operations.
ETL
ETL stands for Extract, Transfer, Load, and it refers to how you extract data from a source, transform it into a plan, and accumulate it into a data warehouse. ETL uses batch processing so that users can analyze relevant data as per their requirements and problems.
It collects data from multiple sources, applies rules to the same, and then loads the data into a database and the visibility of this is across the organization. Hence it becomes an extremely vital skill to have. As the root of all business decisions is data and what information can be derived from this data.
Operating Systems
It is highly important that you are well versed and have the know how to use operating systems like UNIX, Linux, Solaris, and Windows.
Data Mining Tools
Mining and extracting of information for learning the trends along with forecasting is difficult in large data sets. Apart from this, scrutinizing this information and competently using this data is among the toughest skills that are needed to be successful in this path.
Visualization
Assurance of high data accessibility along with the growth of cloud storage is an important skill to learn. it is essential to provide understandings and findings in a simplified form that can be comprehended easily.
Data Engineering is a vast field and always keeping up to date and upscaling yourself can become overwhelming and challenging. The skill sets required are vast and varied and to grow, stay relevant and at the top of you game you will have to put in the extra time and effort. Our courses are designed and streamlined in such and that they will help achieve your goal and help you further your career goals.
India today is an economy that is growing and is a force to reckon with, it is a hub of opportunities, and we are going to see a rising need of data engineers, combined with that top companies are paying good salaries for skilled data engineers. This is the time to upgrade yourself, take up online courses and make use of the rising opportunity to build and advance your career.
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