What is the Difference between the Roles of a Data Analyst, Data Engineer, and Data Scientist

With upsurge of salaries and the competitive job approaches in the IT market, there are signs that the demand for data analyst, data scientist and data engineers will skyrocket. Many industry reports also provide information on how these technology studs are trending in the job market today. According to a survey led by LinkedIn, skills like machine learning and data science were in the list of emerging jobs. Data science is touted as one of the best career indicating that the future is going to be bright for expert professionals in machine learning skills. 

Data has always been a major factor in decision making. The world runs on data and no organization will be able to survive without having a data driven decision or without making strategic plans. Data influences out lives in several different ways, such as social media or maybe major practices in the business domain. Nonetheless, it is essential that we understand the differences in the key role making it possible for us to use the technology we’re using today.  

What is the Difference between the Roles of a Data Analyst, Data Engineer, and Data Scientist

 Most businesses these days are making extensive usage of data analyst, data scientist, and data engineers for different purposes. This trend will continue to rise as organizations are in a massive hunt for individuals skilled in these technologies. Clearly, these job roles have a different significance and hold unique attributes that help businesses growth.  

Most people often get confused between these roles and often believe that they’re interchangeable, but in fact have different attributes each.  

Data analyst or big data analyst 

The role of a big data analyst is to request information from databases or querying. The individual is supposed to process, and leverage datasets providing summarized reports and visuals. Once these datasets are gathered, patterns and conclusions are drawn using algorithms. Although a data analyst uses algorithm, they do not require to create them or have strong mathematical background, however the data analyst requires a to have comprehensive skills in data munging, statistics, exploratory data analysis, and data visualization.  

The skillset required for a data analyst or a big data analyst consists of data warehousing, programing skills (R, Python, JavaScript, C, C++), scripting and statistical skills, database knowledge such as SQL, data visualization, knowledge in spread sheet, and adobe and google analytics. 

Being a data analyst the individual works in simplifying complex data to charts that is relatable. The salary compensation however, is not so great ranging between $55,000-$65,000. 

scientist ranges somewhere between $115,000-$125,000.  

Data Engineer 

As a big data professional or a big data engineer, they’re mostly involved in preparing data. Most of the big data professional work closely with data scientist. The data analyzed and prepared by them gets analyzed by data scientist. The job role mostly consists of developing, constructing and help maintain the architecture used by data scientists.  

The skillsets required to become a big data professional are Hadoop, Spark, Hive, Pig, MapReduce, and SQL.  

At and entry level, the salary compensation of a data engineer or a big data professional ranges between $95,000-$110,000.  

It can be very difficult for someone looking for a big data job without prior credentials or skillsets, given the number of people reskilling themselves in big data. To stay competitive in the job market professionals are now taking up big data certification.  

Data Scientist 

Similar to big data analysts, a data scientist is someone who has the capability to analyze and interpret complex data. However, data scientists use wide range of skillsets such as machine learning, statistics, programming, predictive analytics, and problem solving that further helps come up with valuable insight and decision making.  

They encompass proficiency in capturing data in a manner by creating machine learning algorithms, data mining, cleaning and data alignment.  

Not only a data scientist is expected to create new machine learning algorithms but also handle large sets of data. Their job role does not just end here, they’re required to deliver positive conclusions based on their new findings. The demand for data scientist will keep booming since employers are looking to hire skilled professionals in data science.  

Machine learning, programming skills in R and Python, statistics, mathematics, predictive modelling, data visualization tools such as RapidMiner, Tableau, and ggplots, etc. database knowledge such as SQL or MongoDB are skillsets required by employers today. With demand increasing the salary package of data scientist ranges somewhere between $115,000-$125,000.  

Is it still worth taking up a career in these job roles? With rising demand in the job market, it is evident that the competition will be fierce in the coming years.  

Last updated:5/27/2019 9:39:48 PM
Lucia Adams

Lucia Adams

Lucia Adams is a professional writer, blogger who writes for a variety of online publications. She is also an acclaimed blogger outreach expert and content marketer. She loves writing blogs and promoting websites related to education, fashion, travel, health and technology sectors.


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