Big data is changing the way we do business and creating a need for data engineers who can collect and manage large quantities of data.

Data engineering is the practice of designing and building systems for collecting, storing, and analysing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 Exabyte’s per day by 2025. That’s one and 18 zeroes of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

Everything you need to know about a Career in Data Engineer?

Data engineers are responsible for designing, maintaining, and optimizing data infrastructure for data collection, management, transformation, and access. They are in charge of creating pipelines that convert raw data into usable formats for data scientists and other data consumers to utilize. The data engineer role evolved to handle the core data aspects of software engineering and data science; they use software engineering principles to develop algorithms that automate the data flow process. They also collaborate with data scientists to build machine learning and analytics infrastructure from testing to deployment.

The amount of data an engineer works with varies with the organization, particularly with respect to its size. The bigger the company, the more complex the analytics architecture, and the more data the engineer will be responsible for. Certain industries are more data-intensive, including healthcare, retail and financial services.

Data engineers work in conjunction with data science teams, improving data transparency and enabling businesses to make more trustworthy business decisions.

What Does a Data Engineer Perform?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

These are some usual tasks you strength achievements while operating with data:

1. Acquire datasets that align with business needs
2. Develop algorithms to transform data into useful, actionable information
3. Build, test, and maintain database pipeline architectures
4. Collaborate with management to understand company objectives
5. Create new data validation methods and data analysis tools
6. Ensure compliance with data governance and security policies

Working at smaller companies often means taking on a greater variety of data-related tasks in a generalist role. Some bigger companies have data engineers dedicated to building data pipelines and others focused on managing data warehouses—both populating warehouses with data and creating table schemas to keep track of where data is stored.

What’s the Difference between Data Analyst & Data Engineer?

Data scientists and data analysts analyse data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing that high-quality data. Data engineers gather and prepare the data and data scientists use the data to promote better business decisions.

Let’s talk about the crown analysing between Data Engineer vs Data Analyst:

Why pursue a career in data engineering?

A career in this field can be both rewarding and challenging. You’ll play an important role in an organization’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions.

As long as there is data to process, data engineers will be in demand. In fact, Dice Insights reported in 2019 that data engineering is a top trending job in the technology industry, beating out computer scientists, web designers, and database architects. LinkedIn listed it as one of its jobs on the rise in 2023.

The reality is that so long as data is used in a business to drive decision-making or answer business questions, the demand for data engineers will remain. So if you're interested in pursuing a career in data engineering, there has never been a better time. 

Salary of Data engineers by 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. 

Salary of Data Engineer: Based on Location

Location is another vital factor affecting the average Data Engineer Salary.  Some locations have better job opportunities, more demand for the candidates, and, naturally, becomes a hub of thriving possibilities.

 Salary of Data Engineer 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. 

Salary of Data Engineer Based on Employer 

Another significant factor that decides the average data engineer salary is a company or the employer.  Better companies have better job positions and pay structures for a data engineer.

The fresher’s can also get a good pay base at top-tier companies. 

Some of the Top Companies that hire Data Engineers in India are:

1. Amazon Inc, India
2. Deloitte
3. HCL Technologies Ltd. 
4. Cognizant
5. IBM India Pvt. Ltd. 
6. Tata Consultancy Service Ltd.
7. Accenture
8. Cognizant
9. InfoSys
10. Wipro

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

Data Engineer Salary in Other Countries 

Let us look at the average data engineer salary in other countries.

Data Engineer Occupation and Commitments:

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
2. Data Storing
3. Transformation

Data Engineer Occupation

Now based on these functions, Data engineer Role is categorized into three areas. 

1. Generalist
2. Warehouse-centric
3. Pipeline-centric

Data Engineer Commitments:

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:

1. Develop an architectural design for data platforms
2. Manage and enhance the existing data structures and pipelines for efficiency
3. Customize and develop data integration tools for smooth workflow
4. Interpret data trends for business objectives and goals
5. Structure and manage data and metadata (data that describes about the other data) via database systems
6. Ensure the stability and performance of data pipelines
7. Set up new and updated tools for data extraction and analysis
8. Build efficient algorithms for analysis and interpretation
9. Evaluate business needs and requirements

Data Engineer must have Skills

1. Bachelors or Master’s degree in Computer science, Technology or any other related field
2. Knowledge of Technical skills like Apache Spark, Apache Hadoop, ETL tools, etc. 
3. Proficiency in programming languages like C++, SQL, Python, etc.
4. Basic understanding of operating systems like Linux, UNIX, Solaris, etc. 
5. Project-Based Learning
6. Prior experience is preferred.
7. Clear fundamentals of Data structures, Data Warehousing solutions and Data modelling.
8. Requires soft skills like communication, collaboration and problem solving.

How to grow into a Data Engineer?

Data Engineering is a flourishing field where not only data-driven companies instead every industry, 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

Step 2: Acquire the Skills Required

1. Proficiency in Programming Languages
2. Understand Automation and Scripting
3. Master the Databases
4. Proficiency with Data Processing techniques and solutions
5. Knowledge of Data Structures and Algorithms
6. Soft Skills

Step 3: Build Your Portfolio

Step 4: Advance Professionally

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.

Conclusion:

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 no 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. 

Thank you for taking the time to read this Blog.

Get in Touch to more…………….