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Data Analyst vs Data Engineer vs Data Scientist: Skills, Responsibilities, Salary

aayushi94 profile image aayushi94 ・4 min read

Data Analyst vs Data Engineer vs Data Scientist

Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer and data scientist.

Who is a Data Analyst, Data Engineer and Data Scientist?

Data Analyst - Data Analyst analyzes numeric data and uses it to help companies make better decisions.

Data Engineer - Data Engineer involves in preparing data. They develop, constructs, tests & maintain complete architecture.

Data Scientist - A data scientist analyzes and interpret complex data. They are data wranglers who organize (big) data.

Data Analyst vs Data Engineer vs Data Scientist

Data Analyst
Most entry-level professionals interested in getting into a data-related job start off as Data analysts. Qualifying for this role is as simple as it gets. All you need is a bachelor’s degree and good statistical knowledge. Strong technical skills would be a plus and can give you an edge over most other applicants. Other than this, companies expect you to understand data handling, modeling and reporting techniques along with a strong understanding of the business.

Data Engineer
Data Engineer either acquires a master’s degree in a data-related field or gather a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

Data Scientist
Data Scientist is the one who analyses and interpret complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc.

Skill-Sets

Data Analyst -
Data Warehousing
Adobe & Google Analytics
Programming knowledge
Scripting & Statistical skills
Reporting & data visualization
SQL/ database knowledge
Spread-Sheet knowledge

Data Engineer -
Data Warehousing & ETL Statistical & Analytical skills
Advanced programming knowledge
Hadoop-based Analytics
In-depth knowledge of SQL/ database
Data architecture & pipelining
Machine learning concept knowledge
Scripting, reporting & data visualization

Data Scientist -
Statistical & Analytical skills
Data Mining
Machine Learning & Deep learning principles
In-depth programming knowledge (SAS/R/ Python coding)
Hadoop-based analytics
Data optimization
Decision making and soft skills

Data Analyst vs Data Engineer vs Data Scientist Skill Sets

As mentioned above, a data analyst’s primary skill set revolves around data acquisition, handling, and processing. A data engineer, on the other hand, requires an intermediate level understanding of programming to build thorough algorithms along with a mastery of statistics and math! And finally, a data scientist needs to be a master of both worlds. Data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.

Now that we have a complete understanding of what skill sets you need to become a data analyst, data engineer or data scientist, let’s look at what the typical roles and responsibilities of these professionals.

Next, let us compare the different roles and responsibilities of a data analyst, data engineer and data scientist in their day to day life.

Roles And Responsibilities

Data Analyst -
Pre-processing and data gathering
Emphasis on representing data via reporting and visualization
Responsible for statistical analysis & data interpretation
Ensures data acquisition & maintenance
Optimize Statistical Efficiency & Quality

Data Engineer -
Develop, test & maintain architectures
Understand programming and its complexity
Deploy ML & statistical models
Building pipelines for various ETL operations
Ensures data accuracy and flexibility

Data Scientist -
Responsible for developing Operational Models
Carry out data analytics and optimization using machine learning & deep learning
Involved in strategic planning for data analytics
Integrate data & perform ad-hoc analysis
Fill in the gap between the stakeholders and customer

Data Analyst vs Data Engineer vs Data Scientist Roles

Now data scientist and data engineers job roles are quite similar, but a data scientist is the one who has the upper hand on all the data related activities. When it comes to business-related decision making, data scientist have higher proficiency.

After these two interesting topics, let’s now look at how much you can earn by getting into a career in data analytics, data engineering or data science.

Data Analyst vs Data Engineer vs Data Scientist: Salary

Data Analyst - $59000 /year
Data Engineer - $90,8390 /year
Data Scientist - $91,470 /year

Data Analyst vs Data Engineer vs Data Scientist Average Salary

The typical salary of a data analyst is just under $59000 /year. A data engineer can earn up to $90,8390 /year whereas a data scientist can earn $91,470 /year.

Looking at these figures of a data engineer and data scientist, you might not see much difference at first. But, delving deeper into the numbers, a data scientist can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.

If you wish to know more about Data scientist salary, job openings, years of experience, geography, etc., here’s a full-fledged article on Data Scientist Salary for your reference.

If you are someone looking to get into an interesting career, now would be the right time to up-skill and take advantage of the Data Science career opportunities that come your way.

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