Data Engineer vs Data Scientist- The Differences You Must Know

Data Engineer vs Data Scientist-Explore the similarities and differences between data science careers and make a smooth career transition to data science.

Data Engineer vs Data Scientist- The Differences You Must Know
 |  BY ProjectPro

This blog on Data Science vs. Data Engineering presents a detailed comparison between the two domains. The first two sections briefly overview the two domains and some significant differences. As we proceed further into the blog, you will find some statistics on data engineering vs. data science jobs and data engineering vs. data science salary, along with an in-depth comparison between the two roles- data engineer vs. data scientist.


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Data Engineer vs Data Scientist: Demand

With the rising volume of data and the adoption of IoT and Big data technologies, data scientists and data engineers will be in high demand in practically every IT-based firm. A quick search on LinkedIn shows over 34K open data engineer jobs and 23K data scientist jobs in India alone. The available data engineer and data scientist jobs are approximately 232K and 150K in the US. These statistics on vacant data engineer and data scientist jobs give clear proof of evidence of the increasing demand for these data science job roles worldwide. Let’s explore the subtleties of the data engineer vs. data scientists divide to understand the two most in-demand data-related job roles. We will cover the definitions, similarities, and differences between the two and give some expert advice on getting your career started as a data scientist or data engineer.

Data Engineer Demand in India

Data Scientist Demand in India

Data Engineer Demand in USA

Data Scientist demand in USA

Data Engineering vs. Data Science- Definition

Data Science is an interdisciplinary branch encompassing data engineering and many other fields. Data Science involves applying statistical techniques to raw data, just like data analysts, with the additional goal of building business solutions.

In contrast, Data Engineering consists of creating pipelines to extract and process data to generate valuable business insights.

Below are the detailed definitions of Data Engineering and Data Science-

What is Data Engineering?

It consists of two terms- 'data' and 'engineering'.

'Data' refers to vast volumes of data generated from various sources.

'Engineering' relates to building and designing pipelines that help acquire, process, and transform the collected data into a usable form.

Data Engineering involves designing and building data pipelines that extract, analyze, and convert data into a valuable and meaningful format for predictive and prescriptive modeling.

Data Engineering teams are responsible for maintaining data to make it accessible and usable by others. In a nutshell, data engineers put up and maintain the company's data infrastructure, preparing it for analysis by data analysts and scientists.

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What is Data Science?

"Data Science" is the art of transforming data into actions.

But how does it change data?

It converts data using various tools and technologies and builds data modeling solutions that offer helpful and valuable insights to solve business problems. For example, companies can leverage data-driven business insights to predict customer behavior using algorithms and techniques and enhance overall customer experiences.

Data Science experts use machine learning techniques to create artificial-intelligence-based data models capable of performing activities that usually require human intelligence. These systems generate insights that analysts and business users may turn into real-world commercial value.

Difference between Data Science and Data Engineering

Data Science

Data Engineering

  1. Data Science involves extracting information from raw data to derive business insights and values using statistical methods.

Data Engineering is associated with data collecting, processing, analyzing, and cleaning data. One may use the processed data in other processes like data visualizations, business analytics, etc.

  1. Data Science looks into boosting the performance of a machine learning model.

Data Engineering handles the entire data pipeline's optimization and efficiency for sourcing data from the data warehouse.

  1. Data Science creates and improves statistical analysis and machine learning predictive models for data analytics. It entails generating data visualizations and charts for analysis.

Data Engineering assists the Data Science team by implementing feature transformations with the help of big data technologies on datasets to train predictive models. It doesn't entail creating data visualizations.

  1. Advanced-level understanding of mathematics, statistics, computer science, etc., is required to become a Data Science expert. It is not necessary to have expertise in programming.

Expert-level knowledge of programming, Big Data architecture, etc., is essential to becoming a Data Engineering professional.

Having a broad knowledge of machine learning or statistics is optional.

Data Engineer vs. Data Scientist 

A LinkedIn report in 2021 shows data science and data engineering are among the top 15 in-demand jobs. Due to the COVID-19 pandemic, there has been a slight shift in preference for remote job options. At number 11 is "Specialised Engineers", which covers different kinds of engineers, but this indicates that specialization in engineering is definitely in demand, and a specialization in data engineering is no exception. At number 15, we can see "Data Science Specialists." The statistics reflect the demand and growth for data engineers and data scientists.

Data Engineers and Data Scientists are very closely related professions, and the two vocations must work closely together and constantly communicate to ensure the best results. But what are the differences and similarities between the two roles, and how does an individual looking for a job know which one is better? Let's now look at the two data science job roles- Data Engineer and Data Scientist.

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Data Engineer vs. Data Scientist - The Definition

According to Wikipedia, a data engineer creates big data ETL (Extract - Transform - Load) pipelines for sourcing data using big data infrastructures. They make it possible to take vast amounts of data and translate it into business insights. They are focused on the production readiness of data and things like formats, resilience, scaling, and security. It means that a data engineer is the one who is responsible for gathering large amounts of data relevant to the organization and grouping and storing this data securely in an organized and easily accessible format.

Data Scientist Vs Data Engineer

Wikipedia defines data science as an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. A data scientist creates programming code and combines it with statistical knowledge to develop insights into business data. In other words, a data scientist performs data analysis on data stored in data warehouses or data centers to solve a variety of business problems, optimize performance and gather business intelligence. The data scientist uses the data collected by the data engineer to perform processing and analysis to provide valuable insights to the organization to help build and grow the business.

Who is a Data Scientist?

Data Scientists are professionals responsible for analyzing data relevant to a particular company to provide various insights and recommendations for improving the business. Data scientists take raw data and apply different techniques and machine learning models to make sense of the data. A data scientist may not always be presented with a business problem to solve. Still, they must constantly analyze big data and develop new ways to process and organize data to make sense of it. Finding patterns in the data and giving a business more insight using this data is the primary role of a data scientist. Data scientists are also required to present their findings to various stakeholders in a more understandable manner, for which data visualization skills will have to be applied.

Who is a Data Engineer?

Data Engineers are the professionals responsible for building, maintaining, and managing a business's "big data" infrastructures. A data engineer has to gather, collect, and prepare data across various sources and maintain a database that allows convenient storage, retrieval, and deletion of the data across data lakes. The data engineer has to write complex queries that facilitate data storage in the database and retrieval of data in the desired format. Data Engineers deal more with the design and architecture of a database management system. Maintaining the integrity of the data and ensuring that the database management system is secure from external access while at the same time providing easy access to those within a business who require the data comes under the job description of a data engineer.

What does a data scientist do? vs. What does a Data Engineer do?

By now, it has become clear how the two roles are very closely related. For optimum use of the data, the data engineer and data scientist must work closely together for efficient data processing. A data scientist can only play his part in the work done by the data engineer. Similarly, the work of a data engineer is only beneficial to a business if the data scientist plays his role in the analysis and interpretation of the data. As closely linked as these two roles are, there are some apparent differences between what a data engineer and a data scientist do. Below is an overview of the different expectations from each job description.

Data Scientist vs. Data Engineer: Roles and Responsibilities

Data Engineer vs Data Scientist Skills

  1. The primary role of a data engineer is to design and develop a highly maintainable database management system. The primary role of a data scientist is to take the raw data from the database and use it to provide insights to improve the business.

  2. A data engineer works mainly on the design and architecture of a database management system. In contrast, a data scientist works on applying analytical tools and modeling techniques to process the data.

  3. A data engineer is responsible for transforming Big Data into a valuable form for analysis. The data scientist does the actual analysis of this Big Data.

  4. A data engineer's responsibility is to ensure that the infrastructure meets business requirements and adheres to industry standards. A data scientist may only sometimes be aware of a business problem that has to be solved. Instead, data scientists must analyze the data and develop their approaches to use it to make better business decisions.

  5. A data engineer's responsibility is to ensure that the infrastructure meets business requirements and adheres to industry standards. A data scientist may not always be aware of a business problem that has to be solved. Rather, data scientists must analyze the data and develop their approaches to use the data to make better decisions for the business.

  6. A data scientist also has to ensure that the insights collected from the analysis and processing of the data are conveyed in the proper manner to various stakeholders and the higher management or anyone else responsible for making any business decisions.

  7. A data engineer has to ensure that the data maintained in the databases is safe and secure, especially if there is confidential data. The data engineer also is responsible for providing data backup. Should there be any issue, measures must be available for data recovery and restoration. Data engineers are a gatekeeper for the data. They must ensure that the data is secure from outsiders trying to gain access to the data while at the same time providing seamless access to a data scientist or anyone else within the company who requires the data.

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Data Scientist vs. Data Engineer: Skills

  1. Data scientists and engineers must either have a bachelor's degree in computer science and engineering or a related field like Math, Statistics, or Economics, but it is not a mandate. A strong command of software and the programming field is vital to building, managing, handling, and processing large data sets.

  2. A data scientist has to perform model and process the data using techniques based on machine learning, natural language processing, and artificial intelligence (AI). Hence, these are skills necessary for becoming a good data scientist. The data scientist role also expects an individual to have strong analytical and mathematical skills. 

On the other hand, a data engineer must have a solid database management base. SQL is a very much needed skill. In addition to SQL, a good command of languages like Python and R is an added advantage since data mining is part of a data engineer’s job. A data engineer should be capable of understanding business requirements for a database and should have the skills to design the architecture for a sound database management system and also be able to develop it based on the design.Good knowledge of Spark, Hadoop, and Kafka will help. Utilizing cloud platforms like Amazon web services would also be helpful for a data engineer to manage better the large amount of data involved. Data mining and data management skills are essential for a data engineer.nd data management skills are essential for a data engineer.

  1. A data scientist must have good communication skills so that they can present the observations and insights gained from processing the data to various stakeholders in a manner they can understand. On the other hand, a data engineer can be more proficient from a communication point of view.

  2. A data scientist is expected to have data visualization skills as data scientists analyze data and present it in a graphical and more understandable format. It can benefit both the data scientists themselves and any stakeholders who have to understand the data better. Remember that the human brain processes visual data faster than any other data. According to MIT, 90% of the data transmitted to the human brain is visual. Since data engineers and data scientists usually work very closely together, it could significantly help the data science teams if the data engineering team also has relatively good data visualization skills.

Data Engineer

Data Scientist

Programming Languages like R, Python

Programming Languages like R, Python

Database management skills including SQL

Math, Statistics

Knowledge of big data architecture such as Hadoop, Kafka, Hive, Impala

Knowledge of database queries to access data

Knowledge of security protocols to ensure data is secure.

Machine learning skills.

Cloud computing skills, e.g., Amazon web services.

Communication skills

Data Visualization skills.

Data Visualization skills.

Basics of machine learning added benefit.

NLP, AI added benefit.

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Data Engineer vs Data Scientist: Salary

Here is an overview of salaries for data engineers and data scientists in India and the United States, as per Glassdoor.

Data Engineers Salary in India

Data Scientists Salary in India

Data Engineers Salary in USA

Data Scientists Salary in USA

Taking a close look at the images, we see that for the role of a data engineer in India, the average salary is Rs. 800,000 per annum. A data engineer in the United States earns $112,493 a year.

The average salary of a data scientist in India is Rs 11,00,000 per annum, while a data scientist in the United States makes an average of  $117,212 per year.

Both jobs are the most in-demand job roles in India, the US, and across the globe. So if you are still considering starting with a career in data science or data engineering, then this is the best time to hone your skills.

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How to Become a Data Scientist/Data Engineer?

As we have seen above, the role of a data engineer and data scientist are pretty closely linked. You will find some overlapping skills making it easier for you to make a career transition from data engineer to a data scientist. Nevertheless, here are some ways to start building up your base to kickstart your data science career or a data engineer career:

Data Scientist

  1. Brush up on your skills in machine learning, deep learning, NLP, and AI to add to your resume and help build the learning curve.

  2. Once you understand of the techniques and technologies involved in machine learning and deep learning, remember that it is crucial to have some practical knowledge. Hands-on experience working with machine learning projects in R and machine learning projects in Python, NLP projects, and deep learning projects will give you a competitive edge if you wish to apply for a data scientist job role.

  3. Work on your data visualization skills and know-how to convey your findings to an audience clearly and effectively.

Data Engineer

  1. Constant exposure to SQL and refreshing your knowledge of database management systems are great ways to stay updated and be a prime candidate for the role of a data engineer.

  2. To stand out from the crowd, build your hands-on experience in big data by working more extensively on architectures such as Hadoop, Spark, Kafka, and Impala.

  3. A basic understanding of machine learning would also be very beneficial if you apply for the data engineer role. Remember that data scientists and data engineers work very closely together, and a data scientist will use the data provided by a data engineer to perform various processing. Basic machine learning knowledge will help the data engineer get a better idea of the type and format of data the data scientist requires.

Remember that the best way to learn is with some hands-on experience, no matter which roles you plan on applying for. If you are still looking to get started, or even if you are no longer a beginner in data science and big data, it would be great to fine-tune some of your skills by increasing your exposure to some projects in these fields. That could help you build your skillset, help you find your desired career, and improve your chances of being selected.

Are you a Data Scientist or a Data Engineer?

Hopefully, you now have a better understanding of the differences in roles and responsibilities between a data scientist and a data engineer. Suppose you are keen on applying machine learning, natural language processing, deep learning, and AI to analyze data to find hidden patterns. In that case, becoming a data scientist might be your calling. On the other hand, if your strengths lie in the development, design, and architecture of database management systems and you are interested in handling large datasets, then you should consider applying for data engineering jobs. Try to build your skillset, considering some of the above recommendations to increase your chances of landing a top data gig.

Is data engineering more important than data science?

Data Science and Data Engineering are two separate areas of science. To address day-to-day issues, both data professionals deal with diverse problem areas and require different skill sets and techniques. Most well-established organizations have separate departments for data science and data engineering. However, some organizations do not have respective departments for these domains. Data science experts in these companies must also possess a data engineering skillset and knowledge. This indicates that data engineering skills come in very handy as a data science professional. As a result, we can say that data engineering holds more importance than data science.

Data Engineer vs Data Scientist: Which is better?

The answer to this question depends on the technical background and preferences before entering the tech industry. If you are more interested in cleaning data and building ETL pipelines, then you should pursue the Data Engineer Learning path. On the other hand, if you enjoy analyzing data and are more inclined towards understanding mathematical equations, then you should pursue Data Science Learning Path.

If you still need clarification about which role best suits your background, it would be an intelligent decision to explore some big data and data science projects to gain practical knowledge in the two fields. Some hands-on experience with the different projects from the two categories will also give you some idea of what a job in one of these fields might be like and help hone your data science and data engineering skillset. Big Data Projects will give you a better idea of the kinds of projects that data engineers work on and give you some hands-on experience. Similarly, you can go through Data Science Projects for more of an idea of the projects that a data scientist would work on. This will even help you better understand what to expect if you secure a job in either of these fields.

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FAQs on Data Engineer vs Data Scientist

Is data engineering easier than data science?

No. There are far more resources available for data science than data engineering. In addition, several tools and libraries exist to make data science more accessible. Therefore, when it comes to learning either of them, data science seems easier than data engineering. 

Can data scientists become data engineers?

The two roles- data scientists and data engineers- are different, and it's not easy for a data scientist to become a data engineer. The main reason is that becoming a data engineer requires programming expertise, and data scientists need to gain programming skills to become data engineers. Although the former can gain those skills, it will take a long time, and the return on investment (ROI) will be minimal.

Which is better data scientist or data engineer?

It all depends on the topics that you find interesting. If you prefer diving into mathematically rigorous algorithms, then aiming for the data scientist role will be a better choice. In contrast, if you are more inclined towards building ETL pipelines then, the data engineer career will work for you.

Which pays more data engineer or data scientist?

As per Glassdoor, the average base salary for a data scientist in the USA is $1,17,212 per year, and for a data engineer in the USA, it is $1,12,493 per year. The numbers suggest that the salaries of data scientists and data engineers are comparable.

 

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