Big Data Engineer Salary: Requirements and Responsibilities

Big Data Engineer Salary


In the United States, the national average income for a Big Data Engineer is $104,463. 

Salary of a Big Data Engineer in India 

In India, the average yearly compensation for a data engineer is around 840K. Bangalore is home to many of the country’s data engineering positions, with corporations such as Amazon, IBM, and Autodesk routinely hiring for this role. 

Salary of a Big Data Engineer in the United Kingdom 

The average annual income for data engineers in the United Kingdom is £50,481. Many organizations, including Shop Direct and Tessian, are actively recruiting for data engineers in places like Liverpool and London. 

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Salary of a Big Data Engineer in Toronto, Canada 

Data engineers in Toronto make an average of CA $88K a year. Several organizations in the city, including ScotiaBank and IBM, are presently hiring data engineers. 

Factors that can affect a big data scientist’s compensation 


In many ways, where you choose to work affects your pay. Diverse sectors have different data difficulties and have varying financial resources to pay for a top data scientist salaries. Make sure you’re not limiting yourself by working for a company that won’t pay you what you’re worth. 

The highest pay was paid to data scientists in search/social networking, according to the O’Reilly Salary Survey, which makes sense considering the amount of valuable data those organizations have (think LinkedIn, Facebook or Google). They monitor millions of people’s interactions on their platforms and must draw some logical inferences from the pandemonium. 

Based on 44 employee salaries, the average Facebook data scientist compensation is $133,000 per year. A data scientist at Google earns roughly $145,000 per year. 

Payscale compares and contrasts technology companies like Amazon, which pay around $150,000 in median data scientist wages, with consulting firms like Booz, Allen and Hamilton, which pay less than $100,000 in median data scientist salaries. Based on 26 employee salaries, senior data scientists at Linkedin earn an average of $157,000 a year. 

It’s also worth noting that businesses like Facebook and LinkedIn give significant stock incentives, which may easily increase salary by $40,000 to $50,000. Those firms are still hiring, with Facebook having 16 open data science positions. 

Startups are frequently large hirers, and their pay are a good indicator of where salaries are headed in the future. While only three data scientist jobs paid over $100,000 were available from hardware startups, 71 enterprise software startups were looking to hire data scientists at a salary of over $100,000, possibly indicating a latent demand for data scientists in the industry that will soon spread to larger companies. 


Working in places with a high concentration of top people and thriving businesses has considerable advantages. The United States of America has the greatest median compensation and range for data scientists, according to the O’Reilly Salary Survey. It’s now up to us to locate the most profitable region in the country. 

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Most people consider Silicon Valley to be the obvious choice, and this appears to be true at first glance. Mountain View, where Google and LinkedIn are headquartered, has seen a 23 percent increase in pay, according to Payscale. According to the O’Reilly research, residing in California was worth an extra $16,000 in earnings. 

But that isn’t the whole tale. We haven’t factored in living expenses or state tax rates. 

We’ve compiled a list of the highest-paying areas in the United States for data scientist salaries, as well as cost of living indices for each city, so you can get the full picture (for more on our methodology, see our appendix at the end of the article). 

Degrees and experience 

According to Payscale, 5-10 years of experience is worth $20,000 higher in yearly compensation, 10-20 years is worth $30,000, and more than 20 years is worth $55,000. 

According to a search for data scientists in San Francisco, 42 percent of data scientists earn more than $100,000, with 68 percent of senior data scientists earning more than $100,000. Experience, probably even more than degrees, is important in data science. To get your foot in the door, you’ll almost certainly require an advanced technical degree: 

A Ph.D., on the other hand, can quickly become a significant time commitment (the average duration from start to dissertation is 8.2 years), and in that time, if you aimed for at least five years of expertise in data science, you’d get double the annual wage return. Even yet, without a Ph.D., you won’t be able to optimize your data scientist pay.

What does it mean to be an analytics engineer? 

End users are given clean data sets by analytics engineers, who model data in a way that allows them to answer their own queries. An analytics engineer spends their time transforming, testing, deploying, and documenting data, whereas a data analyst spends their time evaluating data. The analytics code base is subjected to software engineering best practices such as version control and continuous integration. 

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What is the distinction between a data engineer and a big data engineer? 

Data is the fuel of the twenty-first century, and we are living in the age of the data revolution. Various data sources and technologies have grown over the previous two decades, with NoSQL databases and Big Data frameworks being the most prominent. 

With the introduction of Big Data in data management systems, the Data Engineer’s function has been updated to Big Data Engineer, which requires them to handle and manage Big Data. The entire data management system is growing increasingly sophisticated as a result of Big Data. To construct, design, and maintain processing systems, Big Data Engineers must now grasp numerous Big Data frameworks and NoSQL databases. 

As we progress through our Big Data Engineer Skills blog, we’ll learn about the duties of a Big Data Engineer. This will assist us in mapping Data Engineer tasks to required skill sets.

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How to become a Big Data Engineer? 

After earning a bachelor’s degree, you can start working on projects. 

A bachelor’s degree in computer science, software or computer engineering, applied math, physics, statistics, or a related discipline is required for entry into this field. To even qualify for most entry-level roles, you’ll need real-world experience, such as internships. If you plan to major in something other than these disciplines in college, make sure you take courses in data structures, algorithms, database management, or coding. It’s critical that you study everything you can. 

Join a study group, go to a hackathon with your pals, or work on personal projects with your classmates to build a portfolio that you may present to potential employers later. 

Sharpen your abilities in analysis, computer engineering, and big data. 

You’ll need to brush up on SQL, one of the most common programming languages used by data engineers. Because most data is kept in relational database systems, this is required. Engineers query data with SQL, and then analyze it with SQL engines like Apache Hive. 

Other programming languages that aid statistical analysis and modeling, such as Python or R, should be familiar to data engineers. A working knowledge of Spark, Hadoop, and Kafka will also come in helpful. 

Other talents may include database architectures, machine learning, data warehousing solutions, data pipeline construction, data mining, and cloud platforms such as Amazon Web Services. 

Because data management technology is always changing, data engineers must keep their finger on the pulse of what’s going on in their industry. 

Get your first engineering job as an entry-level engineer. 

Your first job may or may not be in engineering, but even if it is in IT, you can learn a lot about how to handle data organization problems. That first job will force you to think outside the box and come up with novel solutions to challenges. What is the significance of this? Data engineers don’t do everything by themselves, as you’ll shortly discover. Instead, they pay attention to management, data scientists, and data architects because this is a collaborative field. You may also obtain a grasp of how your chosen industry operates in the real world, as well as how data may be collected, analyzed, and used, through this encounter. 

Study computer science, engineering, applied mathematics, physics, or a related topic in higher education. 

Many engineers excel without a master’s degree in computer engineering or computer science, but if you want to fine-tune your abilities, broaden your knowledge, or work as a data engineer or data scientist, you might consider pursuing a master’s degree in computer engineering or computer science. 

A master’s degree in data engineering isn’t required for every employment. In lieu of a higher degree, some employers are willing to accept relevant job experience and confirmation of technical expertise. 

What Exactly Is Data Analytics Engineering? 

Many people believe data analytics is difficult, and with good reason: it is a complex profession that necessitates extensive research as well as significant technological and mathematical skills. 

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You’ll also need to learn industry best practices, processes, and principles if you want to be a good data analyst, because analyst employment isn’t the kind of job where you can afford to make mistakes. 

Given that one of your primary responsibilities as a data analyst will be to examine key performance measures, interpret data, and design effective strategies for enhancing organizational performance, it will be critical that you carry out your duties with care. 

But that doesn’t mean you won’t be able to learn how to be a great data analyst; it just means you’ll have to devote some time to researching industry best practices and methods. 

Enrolling in an accredited bachelor’s or master’s degree program that will teach you everything you need to know to become a successful data analytics expert is the ideal approach to get up to speed quickly and assure that you’ll be effective in a related career. 

And the good news is that graduating from a reputable analytics program should qualify you for job chances in practically any field.

Responsibilities of a Data Engineer 

Data ingestion is the process of gathering data from diverse sources and ingesting it into a data lake. There are numerous data sources available, each with its own format and structure. 

Data Engineers must be able to extract data from a source efficiently, which can involve batch and real-time extraction methods. Other abilities, such as incremental loading, loading data in parallel, and so on, can help make data ingestion more efficient. 

When it comes to the Big Data world, data ingestion becomes more complicated as the volume of data grows faster, and the data is available in a variety of forms. To capture and inject more data into the data lake, the Data Engineer must also be familiar with data mining and various data ingestion APIs. 

Transformation of data 

The data is always in raw format, which means it can’t be used right away. Depending on the use-case, it must be transformed from one format to another or from one structure to another. Depending on the diversity of data sources, data formats, and necessary output, data transformation can be a simple or difficult operation. Depending on the complexity, structure, format, and volume of the data, this may include a variety of tools and bespoke scripts in various languages. 

Performance Enhancement 

It takes a lot of effort to create a system that is both scalable and efficient. A Data Engineer must be able to improve the performance of a single data stream while also optimizing the overall system. 

When dealing with Big Data platforms, performance becomes even more important. Big Data engineers must ensure that the entire process, from query execution to data visualization via reports and interactive dashboards, is streamlined. Partitioning, indexing, de-normalization, and other concepts are required. 

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Aside from this, Data Engineer jobs have a wide range of tasks depending on the tools and technology used in the sector.


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