Data Engineer vs Data Scientist: 14 Interesting Facts To Know

According to David Bianco, to
construct a data pipeline, a data engineer acts as a plumber,
whereas a data scientist is a painter. Most people think they are
interchangeable as they are overlapping each other in some points.
But, there is a crucial difference between data engineer vs data
scientist. Harvard Business Review outlined the data scientist job
as ‘one of the sexiest jobs of the twenty-first century.’ However,
data engineer job is most demanding rather than data
scientist. ^[1]

Data engineers work with data and develop these data in
such a way that they are useful for others. On the other hand,
data scientists^[2]
transform raw data into knowledge. So that enterprises can use this
knowledge to bring their business to a competitive edge.

Data Engineer vs Data Scientist:
Interesting Facts

The task of a data scientist is to draw insights and
extract knowledge from raw data by using methods and tools of
statistics. This raw data can be structured or unstructured. 
Contrary, the task of a data engineer is to build a pipeline on
moving data from one state to another seamlessly. Below, we
are highlighting the 14 exciting facts between data engineer vs.
data scientist.

1. What is Data
Science and Data
Engineering?

Data science is a multi-disciplinary field that is
encapsulated with several fields like mathematics, computer
science, statistics, and so forth. The primary goal of this field
is to extract insights and knowledge from raw data. Big Data^[3]
and Data Mining are related to this field.

On the other hand, Data Engineering can be referred
to as Data Infrastructure or Data Architecture. The objective
of this field is to develop a large-scale system, MapReduce
applications, and high-scale distributed architecture for big
data.

2. Who is a Data Scientist
and Data
Engineer?

A Data scientist is the one who processes and analyses
data. He analyzes data to make insights into data. In one word, a
data scientist is someone who knows mathematics and statistics with
programming skills to extract knowledge from complex data and
finally build a mathematical model.

A data engineer is someone who prepares data for analysis.
He collects data from single or multi-sources, stores these data,
and does real-time or batch processing, and serves it through API.
In one word, the difference between them
is that data scientist only knows about data. The data
engineer builds a pipeline to transform data into formats. Then a
data scientist uses that format.

3. Technical Skills
Set

A data engineer prepares data for further analytical use. The
tasks of a data engineer may vary company to company. But, in a
general term, a data engineer develops data pipelines to take out
data from multiple sources and then cleans and integrates these
data.

A data engineer must have to expert in some areas like programming languages^[4], for example, Java,
Scala, Python^[5], and hardware related
knowledge. Mathematical and statistical knowledge is not important
to him.

A data engineer should also know how to build a distributed
system. A data engineer must have to know data warehousing and ETL.
ETL is the combination of three phases, i.e., Extraction,
Transformation, and Loading. The extraction phase allows us to
extract data from multiple sources; the transformation phase
transforms these extracted data into the desired format and finally
loads them into a single source.

On the contrary, a data scientist is responsible for collecting
and interpreting a large volume of data. So, a data scientist must
have to expert in machine learning, deep learning, mathematical,
and statistical knowledge. Hardware related knowledge is not
important to him.

4. Responsibilities

The data engineer constructs, designs, integrates, and
optimizes data from several sources. He makes an architecture for
large databases, and also he tests and maintains it. The main task
of a data engineer is to build a data pipeline by integrating big
data techniques.

On the other hand, a data scientist is responsible for
analyzing data using mathematical and statistical techniques^[6]. A data scientist has to
keep good programming skills to create and integrates API. Also, he
has to keep knowledge about big data eco-system and distributed
system. 

In one word, the difference between data engineer and data
science is that a data engineer develops, tests, and maintains
databases, and a data scientist cleans and organizes
data.

5. Educational
Background

In this criteria, there is a distinction between data
engineer vs. data scientist as well as the overlap between them.
Both are from computer science and engineering background. This
study area is common for both. Besides this, Data engineer occupies
programming knowledge like Java, C++, Python^[7].

On the other hand, data scientists possess Math, Physics,
Economics, and Statistics. Data scientists have knowledge about
business acumen than data engineers. Data engineers possess only
engineering knowledge.

6. Job
Profile

The job profile is one of the major differences between
data engineers and data scientists. The job of a data scientist is
to turn raw data into valuable insights. He applies his knowledge
to solve crucial business problems. His main function is to extract
knowledge from data by using the statistical model.
They organize big data and also remove noises from
them.

On the contrary, a data engineer is one who builds
and maintains a large scale processing system. A data engineer is
like a software engineer who designs and combines data from
multiple sources. His main function is to write queries to access
data effectively and efficiently.

A data engineer develops APIs for extracting and analyzing
data from multiple sources. The objective of a data scientist is to
develop a data flow and retrieval system. He designs and optimizes
the performance of the big data ecosystem.

7. Tools and
Software

Tools and software is another significant difference
between data engineer vs. data scientist. The analytical skills of
a data scientist are advanced than data engineer skills. A data
engineer works with data. In this data, there might be errors or
noise or duplicate data. The data engineer implement several ways
to remove data redundancy. To work with data, they use Redis,
Sqoop, MySQL, AP, Cassandra, Hive, MongoDB, Oracle, DashDB, Riak,
neo4j.

On the other hand, data scientists leverage machine learning^[8]
and statistical methods to deal with already processed data. They
use their statistical or mathematical background with programming
skills to extract knowledge from data. To do this task, they use
RStudio, Jupyter, and so forth.

8. Data Engineer vs Data
Scientist: Salary

Data engineers and data scientists both are playing an
important role in a firm. Salary is one of the major differences
between data engineers and data scientists. The average salary of a
data engineer is higher than the data scientist. Data engineers
earn up to $90,8390 per year. On the other hand, data scientists
earn $91,470 per year.

9. Usages of Programming
Languages

The programming skills of a data engineer is advanced than
the data scientist skills. A data engineer has advanced programming
language skills and machine learning knowledge. Apart from these
skills, a data engineer must have to keep data architecture and
pipeline skills to arrange, build, and design data. A data engineer
integrates data from a variety of sources. 

A data engineer must have to know NoSQL, SQL for database
management. For Big Data infrastructure, he should know Hadoop,
Hive, MapReduce. He needs to know programming
languages to solve critical problems. Moreover, he needs to know
cloud-based data solutions like RDS, EMR, EC2, AWS, and
Redshift.

On the other hand, the data scientist must have to know
how to handle different sizes datasets and also know how to run his
algorithm effectively and efficiently over large datasets. He
should also know relational databases like MongoDB, Couch as well
as NoSQL databases.

A data scientist should know how to analyze third-party
providers’ data. A data scientist must also know programming
languages and big data tools and software^[9], i.e., Hadoop, Python,
Apache Spark, R programming language^[10], etc.

10. Hiring: Data Engineer vs
Data Scientist

The name of companies who hire data engineers is
Bloomberg, Spotify, The New York Times, and Amazon,
PlayStation, Facebook, and Verizon. On the contrary, the companies
that currently hired data scientists are Microsoft, Dropbox,
Walmart, Deloitte, and so forth. There are almost 85,000 job
offerings for data engineers; on the other hand, there are about
110,000 for data scientists.

11. Career Path: Data
Engineer vs Data Scientist

To develop a career as a data engineer, one must have a
bachelor degree in Computer Science & Engineering (CSE) or
information systems. Also, he should pursue data engineering
testifications such as IBM Certified Data Engineer or Google’s
Professional Data Engineer. His career path will be started as a
data engineer, then he will be promoted as a senior data engineer,
and then as a BI architect and lastly as a data architect. In
short, the career flow is: Data Engineer ->
Senior Data Engineer -> BI Architect -> Data Architect.

On the contrary, to develop a data scientist career, one
must pursue an M.S or Ph.D. degree in CSE, mathematics. A data
scientist will start his journey as a junior Data Scientist, then
as a data scientist, and then as a senior data scientist and
finally as a chief data scientist. In short, the
Career stages are: Junior Data Scientist -> Data
Scientist -> Senior Data Scientist -> Chief Data
Scientist.

12. Examples of Work: Data
Engineer vs Data Scientist

The difference between a data engineer vs. data scientist
in their example of working. As far as we know, the
output/objective of a data scientist is to construct a data
product. So, the example of a data scientist’s work can be a
recommendation engine or can be an email filter to identify the
spam and non-spam emails. The example of a data engineer’s work can
be extract tweets from twitter to store into a data
warehouse.

13. Functions: Data Engineer
vs Data Scientist

There is a significant difference between data engineer
vs. data scientists in their functions. To develop any system, data
needs to be analyzed. Basically, data scientists work at this
point. Data scientists work with data architecture or
infrastructure. But they don’t develop it. A data engineer develops
it.

Data scientists build a model using statistical or machine
learning approaches to extract knowledge from data or analyze data.
They develop a data visualization model. Data engineers employ
feature transformation approaches on the datasets. They do not work
with data visualization.

14. Goal: Data Engineer vs
Data Scientist

The goal of a data scientist is to find out ways of business
efficiency. Also, they find out ways of improving profits and
customer experience. In comparison, the goal of a data engineer is
to develop automated systems and models. Their goal is a
development and task-oriented. They develop data pipelines and
tables to provide an analytical task.

Ending
Thoughts

There is a core difference between data engineer vs data
scientist. Basically, a data engineer transforms data without using
machine learning methods, whereas a data scientist uses
machine learning
methods to build a model. Though data
scientists are responsible for analyzing data, they are dependent
on the data engineers to enrich data. Both jobs are demanding in
this modern era as the application of machine
learning, and IOT is increasing
day by day. ^[11][12][13]

If you are a beginner in this field, you may go through
our previous distinctions based article like data science vs. machine
learning and data mining vs. machine
learning. If you have any suggestions or queries,
please leave a comment in our comment section. You can also share
this article with your friends and family via Facebook, Twitter,
LinkedIn, Pinterest, etc. ^[14][15]