Top 10 Big Data jobs in 2020 (Data Science)

Want to consider a career move into big data (data science)? find out the top 10 Big Data (Data Science) jobs are in 2020.

Wondering what are the top 10 big data jobs in 2020 first we must understand breifly how big data has grown and what roles to consider.

Data Science has been ranked as the number one job for the previous four years in a row. As we move towards the digital age and companies go through digital transformations, all kinds of organizations not only large companies present opportunities in this field. The U.S. Bureau of Statistics has reported that Data Science demands would contribute to 27.9 percent increase in employment.

Data Science stands at the crossroads of various algorithms, tools, and machine learning methodologies. Here are some of the vast range of opportunities people with all sorts of skillsets can explore in the area.

10 big data jobs

1. Data Scientist

It is a fairly new career path, and one of the fastest growing job as well. As per LinkedIn Data Science is predicted to create 11.5 million jobs by 2026.

Data Science is applied on huge array of industries, from banking, telecom to health-care thereby providing abundance of opportunities for a career that requires a specific skill-set which a lot of people are not yet skilled in yet.

Thus, with the rising demand the supply of data scientist is not yet sufficient enough which makes it one of the highly paid and sought-after careers.

 A data scientist may do some of the tasks similar to a data analyst. However, they also work on finding, cleaning and organizing large amount of complex data both raw and processed, as well as building machine learning models for predicting future trends based on historical data.

As a data scientist you could be asked to analyze how a change in marketing strategy could affect your company, or to study customer behavior to tailor products for enhanced customer experience. Furthermore, Data Science can prove to be a lifesaving asset, advancement in machine learning have made it easier to detect tumors in early stages, among some of the other benefits in the healthcare sector.

They are highly in demand by companies like Google and Microsoft.

Average Salary $ 113,436

2. Machine Learning Engineer

Artificial Intelligence has quickly become one of the most dominant field in the technological sector, it is estimated to create 2.3 million jobs in 2020. As a Machine Learning Engineer your primary responsibilities include building systems and machines powered by artificial intelligence.

With an estimated data creation to reach 175 zettabytes by 2025, a data-related field like machine learning presents itself with vast employability opportunities. Machine Learning has applications across all sorts of industries from services industries to product manufactures. A Machine Learning engineer helps a company achieve its key performance indicator targets.

A Machine Learning Engineer needs to have essential computer programming skills along with expertise in Python, Java, C and C++ programming languages as well as strong statistical knowledge.

They not only design and build machine learning systems but also need to run tests to monitor the performance and functionality of such systems. As a Machine Learning expert, you would need to be up to date on the best practices to ensure there is no impact on business with emerging trends.

Average Salary $ 111,297

3. Data Engineer

On the advent of companies globally digitally transforming their infrastructure, the rise of application of data science and with AI emerging as the leading technology in all areas there is an ever-increasing need of Data Engineers to allow companies to extract data in a way there are able to derive meaningful insights from them. A Data Engineer would be required to perform processing on gathered and stored data.

They are primarily responsible for managing a company’s data infrastructure, the job role requires more of software development and programming skills as apposed to statistical or machine learning expertise. They act as a bridge between gathering useful data such as sales and revenue and transforming them into useable format for data analysts and scientists.

Average Salary $ 92,054

4. Data Analyst

As machines replace humans in modern day for data gathering, and data creation being in ever rise with use of smart phones, social networking among other areas contributing to it. There is a dire need to hire professionals to manage the data and transform it into meaningful information. As per World Economic Forum by 2022, 85% of the companies worldwide would proceed to implement big data and analytics technologies. Also, that 96% of the companies plan to hire relevant professionals in such areas.

A Data Analyst typically studies business questions and determines if any insights can be driven from available data. They are primarily responsible to monitor entire data gathering processing from collection, to analysis and to design reports as per user requirements. A Data Analyst transforms large data sets to cater to the company’s requirement and to aid them in decision making process by building on insights by available information. Some of the languages they are proficient in are Python, SQL and C.

Average Salary $ 60,391

5. Data Architect

Data Architects lay down the data infrastructure for the company, they have primarily grown from the position of Data Engineers. They translate the business requirements to technological requirements. They not only create new architectures but also work on enhancing performance of existing ones.

Data Architects master in technologies like Hive, Pig and Spark.

Average Salary $ 117,175

6. Statistician

Statisticians use their knowledge in theoretical statistics and methodologies to collect, analyze and interpret data in order to identify trends. They are skilled to handle all variety of data and use their quantitative knowledge to gain better insights.

Companies like Target, Facebook, Twitter all hire statisticians to gain insights about their customers/ users.

Average Salary $ 73,184

7. Database Administrator

A Database Administrator would be a key role that would continue to exist as long as we continue working with data. They ensure database is available to all relevant users, it is properly backed up and to ensure recovery in case of any failures.

Database Administrators typically work on languages like SQL, Java and Ruby on Rails.

Average Salary $ 73,918

8. Business Analyst

This job role is fairly less technical and requires basic tools like MS Office, visualization tools like Tableau and basic business intelligence and understanding. A business analyst is typically the middle man between business users and technological department.

Business Analysts are hired by most leading industries like Uber and HP.

Average Salary $ 79,33

9. Business Intelligence (BI) Developer

Business Intelligence developer is a role constantly rising in demand. Each company would require professionals who understand Business Intelligence. A BI developer would be required to develop, deploy and maintain BI interfaces.

Average Salary $ 101,571

10. Data and Analytics Manager

The Data Analytics manager sets the direction of the data science team, they need to be skilled in SQL, R, SAS among other languages. As well as have leadership and project management experience. Companies like Coursera, Oracle all are looking for Data Analytics manager.

Average Salary $ 95,384


The demand for Data Science professionals does not seem to be diminishing anytime soon in the near years. As we proceed to produce more data and enter this digital age, there will be a need for Data Science professionals in all kinds of industries. As we see an emergent trend of acquiring skills through platforms like Udemy and Coursera the skills required can be acquired right from your own home. Additionally, top U.S. schools offer specializations in the fields as well. Just explore your areas of interests, academic background and the best fit career path for yourself!

Read More: How is Machine Learning used to Analyze Big Data?

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