Data Engineer vs Data Science

In the 21st century, Data is both - the currency of the century as well as the most in-demand commodity. This increase in the value of data has also led to an unprecedented rise in the demand for talent who can make the most effective use of this data. In fact, roles such as Data Scientist and Data Engineer, are among the most desirable employment roles today, with highly scalable growth opportunities. In this article, we dive deep into the topic of Data Engineering vs Data Science and explore their roles and responsibilities, the skills required, and the tools commonly used in the two roles. We then move forward toward the exciting career opportunities of both the roles and answer some of the common questions that you may be wondering about.

What is Data Engineer ?

Data engineering is the process of developing and constructing large-scale data collection, storage, and analysis systems. It's a wide-ranging discipline with applications in almost every industry. 

Data engineers create and construct pipelines that modify and transmit data into a useable format to be interpreted by Data Scientists or business analysts. These pipelines are capable of gathering data from a multitude of sources and consolidating it into a central warehouse.

What is Data Science ?

Data science is a discipline that blends subject matter expertise, computer skills, and mathematical and statistical knowledge to extract useful insights from data. 

Data scientists identify what questions their team should be pursuing and then figure out a way to obtain the answers using data. They frequently create and make use of predictive models for theorizing and forecasting. To put it simply, Data Scientists use data to answer complex issues by processing, modeling, analyzing, and drawing conclusions.

Data Engineer vs Data Science

The main difference between Data Engineers and Data Scientists is their area of focus. Data Engineers are primarily concerned with constructing the framework and infrastructure for the collection, transformation, and storage of data. Data Scientists, on the other hand, are mostly engaged with performing complex mathematical and statistical analyses on the obtained data. 

Data engineers design open data pipelines that enable real-time analytics solutions on complicated data using Big Data tools and technology. Data Engineers also write complex queries to enhance data accessibility. They do the grunt work that allows Data Scientists to perform analytical actions on the data.

Data Scientists employ Data Engineers' data structure and a variety of analytical methodologies to ask and answer relevant questions, uncover hidden patterns, and hypotheses, and come to appropriate conclusions.

Let us sum up the key differences between a data engineer and a data scientist:

  • A data engineer gathers data from various sources and integrates it whereas a data scientist analyzes this data.
  • A data engineer ideally deals with raw data whereas a data scientist works with data altered by a data engineer.
  • A data engineer is not a decision-maker whereas the final analysis report of a data scientist is considered by the company.
  • A data engineer needs to be well-versed with programming languages like Java and Python whereas a data scientist must hold expertise in R and Python.
  • Lastly, a data engineer should have exceptional hold in software development, creating data pipelines, and handling databases and processing systems. However, a data scientist must have hands-on experience in Machine Learning, Artificial Intelligence, developing specialized models, and working with clean data sets.

Now that we have a basic idea of the differences, let us look at these comprehensively and one by one.

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Roles and Responsibilities

Let us now discuss the roles and responsibilities that each of the job profiles is entrusted with.

What does a Data Scientist do?

Some of the most important responsibilities that a data scientist must be proficient with are:

  • Improving data collection techniques to ensure that all relevant data is collected in order to construct analytic systems
  • Collect useful data from available data sources by using data mining techniques
  • Finding patterns and solutions through analyzing massive volumes of data
  • Propose ideas and strategies for dealing with company issues.
  • Make use of Machine learning technologies to choose features, develop classifiers, and optimize them.
  • Developing machine learning algorithms and prediction systems
  • Conducting preprocessing to cleanse unstructured as well as structured data
  • Presentation of results in a clear and concise manner
  • Coordinate with the IT and business teams.

What does a Data Engineer do?

A Data Engineer is entrusted with a number of crucial tasks, some of which are as follows:

  • Plan, construct and provide support to data architectures using a methodical methodology that is in line with business objectives.
  • Obtain information from the appropriate sources. Data engineers store the optimized data after constructing a set of dataset operations.
  • Conduct industry research to handle any concerns that may develop when dealing with a business challenge.
  • Ensure availability of data so that businesses can utilize it to enhance their efficiency.
  • Construct and sustain data pipelines, as well as databases.
  • Collection and administration of data, translating it into valuable information
  • Develop new data validation techniques and analytical methods.
  • Design, construct, assess and keep up-to-date data management systems.
  • Coordinating with management to have a better understanding of the organization's objectives.

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Each of the disciplines requires a particular set of skills for optimum output. We will now list the expected skills for each role.

Data Science Skills:

  • Programming
  • Cloud computing 
  • Data wrangling
  • Database management
  • Data visualization
  • Probability & statistics
  • Multivariate calculus & linear algebra
  • Machine learning & deep learning

Data Engineer Skills

  • Programming 
  • Distributed systems
  • System architecture
  • Database design and configuration
  • Interface and sensor configuration


Both Profiles require the assistance of various tools and proficiency in the following will definitely enable you to accomplish your tasks efficiently:

Tools used by Data Engineer

Data engineers make use of modern programming languages such as:

  • Python
  • Java
  • Scala
  • C++
  • SQL

Data Pipeline tools and Distribution systems such as:

  • Pentaho
  • IBM InfoSpare DataStage
  • Apache Kafka
  • Talend

As well as, Big Data Frameworks, such as:

  • Apache Hadoop
  • Apache Spark

Tools used by Data Scientist

Programming languages such as Python and Java are also used by Data Scientists. In addition, Data Scientists make use of advanced analytical as well as BI tools such as:

  • Rapidminer
  • QlikView
  • Tableau Public
  • Splunk

Data Scientists also have a heavy reliance on various Machine Learning  libraries such as:

  • PyTorch
  • TensorFlow
  • Apache Spark
  • Keras
  • DLib
  • Theano
  • Caffe

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Salary Package

With the current push toward the digital economy, there has been a tremendous increase in accessibility to data. The Big Data concept is here to stay and therefore, it comes as no surprise that both Data Engineers as well Data Scientists are currently highly sorted after job profiles. Both these profiles have a still climbing and growing career trajectory with lucrative annual salary packages.

Data engineers make roughly Rs.7,30,000+ LPA in their early careers (1-4 years of experience). A Data Engineer's compensation rises to over Rs.1,200,000 LPA as they advance to mid-level (5-9 years of industry experience). Data engineers with more than 15 years of expertise might earn upwards of Rs. 1,600,000 LPA.

As for Data Scientists, a person with less than a year of industry experience can make approximately 500,000 per year. Data scientists who possess 1 to 4 years of experience may expect to earn about 610,811 per year. A mid-level data scientist commanding 5 to 9 years of industry experience could rake in an LPA of over Rs. 1,000,000. A Senior-level data scientist in India currently, earns roughly over 1,700,000 LPA as their expertise and talents increase.

Career Paths

Having learned about both the disciplines, let us now look at what career path you could expect to see in either profession.

Career Path for Data Engineer

Data engineering ideally is not a beginner level job role. This implies that any data engineering professional will start their career as a software developer or an analytics role. Professions that provide them with the knowledge required to pursue a career in Data Engineering.

Numerous data engineers utilize roles like database developer, data architect, and solutions architect to hone their data skillset, obtain a deeper understanding of data processing and distributed technologies, and practice with data layers and ETL. Before moving into data engineering, some people may also begin working in data analytics to gain a better understanding of the requirements of a data scientist and analyst.

Career Path for Data Scientist

Data scientists often begin their careers in a beginner-level data science position, either via an internship or a novice data scientist position. This entry-level employment allows young data scientists to hone their technical abilities and work on practical tasks provided to them before moving on to creating their independent experiments and tackling more complex business problems.

Data analysts frequently transition into data science professions via self-teaching data science skills or registering for an online class or Bootcamp.

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Data engineering helps data scientists to concentrate solely on analysis, so saving time and effort, and thereby making them more productive. As a result, both are critical components of any data set's conclusion. They are complementary to one another, not opposed to one another. Data Engineers work with raw data and are responsible for its accuracy, whilst Data Scientists work with altered data provided by Data Engineers and provide a link between customers and stakeholders. 

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Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.

Data Engineer vs Data Science FAQ's

Yes! Although the two professions employ the use of two varying sets of skills, obtaining proficiency in mathematics and statistics could significantly bridge the gap. In addition, you would also want to gain expertise in various tools that are relevant to a data scientist.

Both Data Science and Data Engineering are very lucrative career options with high potential for quick professional and economic growth. Choosing a career path would involve a study of the differences between data science and data engineering, your proficiencies, and in the end, success would be decided based on your hard work!

Some people believe that becoming a Data Scientist is difficult and that the work function of a Data Engineer is complicated. However, whether any job role is easy or difficult is entirely dependent on your preferences, skills, and experience with various technology. Assess your proficiencies and inclinations, match them with the skills required in each profile and go with the one that suits you the best.


  • It is currently in high demand among employers.
  • Data Science is one of the most highly paid jobs.
  • With numerous real-life applications,
  • Data Science is a very versatile field


  • It has a steep learning curve
  • Data Science is a blurry term and may include in its data analysis, data preparation, etc.
  • Data Science is difficult to master as it requires the employment of various skills such as machine learning, programming, business strategies, statistics, etc.