Home Data Science Data Engineer vs Data Scientist: 14 Interesting Facts To Know

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.

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 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

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 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


data science skillset

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, for example, Java, Scala, Python, 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. 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


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.

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


languages-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 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


programming language

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, i.e., Hadoop, Python, Apache Spark, R programming language, 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


career path of 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


example of a 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. 

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.

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