Data Engineer Vs. Data Scientist: What’s the Difference?

Nowadays, the Data Science space is considered an excellent career path for many professionals. The field has many roles,and it is very important to know about those if you are thinking of making a career in this field. Data engineer and data scientist are two important roles that come under this space.

In this article, we are going to discuss the differences between these both job roles that you need to know. They both have their own requirements, responsibilities, and expertise to help organizations improve their business. If you are planning to work in any of these job posts, then this article can be helpful for you.

Who Is A Data Engineer?

A data engineer is a professional who builds data infrastructure and architecture for analysis. The main focus of this professional is to make ready the required data and things. Data engineers commonly belong to the software engineering field,so they are good in several programming languages such as python, java, Scala, etc.Along with programming skills, some data engineers also have a degree in mathematics and statistics. Because of this, data engineers can work in different analytical job roles. Although they have experience in analyzing lots of data, their main focus is to help data scientists to handle a large amount of data effectively.

Data engineers create scalable and understandable infrastructure using raw data to give a valuable intuition to data scientists. Apart from that, they execute complex analytical projects and create practical analytical solutions. Simply we can say that data engineers work to support the analytical tools that are used by data scientists.

Data Engineer Vs. Data Scientist

Who Is A Data Scientist?

Data scientists focus on discovering new insights from the data infrastructure that is prepared by data engineers. This way, both data scientists and data engineers work together to achieve business goals. Also, before the creation of the data engineer role, the works of a data engineer was done by data scientists.Although they work together, these roles are not similar. Like data engineers, data scientists also have programming skills,but data engineers have more expertise in this field than data scientists. Mostly, data scientists learn programming skills to achieve their complex analysis tasks.

Generally, these professionals have a better skillset in data analytics,which data engineers don’t require. Although they need the data infrastructure in their work, they have nothing to do with building and maintaining that data infrastructure. Specifically, data scientists are responsible for managing high-level business operations and markets by using various machines and methods. They use several analytical tools such as Hadoop, SPSS, advanced statistical modeling, and R. Therefore, we can say that data scientists depend on data engineers in their work.

Difference Between Data Engineer and Data Scientist

Criteria Data Engineer Data Scientist
Primary Focus Design, construct, and maintain architecture for data generation, transformation, and storage Analyze and interpret complex data sets to inform business decision-making, predictive modeling, and machine learning
Role Description Develop, construct, test, and maintain architectures (e.g., databases, large-scale processing systems) Utilize statistical, mathematical, and programming techniques to analyze and interpret data, develop algorithms, and build models
Skills Required Database management, ETL (Extract, Transform, Load) processes, Big Data technologies, programming (e.g., Python, Java) Statistical analysis, machine learning, programming (e.g., Python, R), data visualization, domain-specific knowledge
Tools and Technologies Apache Hadoop, Apache Spark, SQL, NoSQL databases, ETL tools (e.g., Apache Nifi), cloud platforms (e.g., AWS, Azure) Python libraries (e.g., Pandas, NumPy, scikit-learn), R, SQL, machine learning frameworks (e.g., TensorFlow, PyTorch), data visualization tools (e.g., Tableau)
Education Background Typically computer science, information technology, or a related field Varied backgrounds including computer science, statistics, mathematics, engineering, or domain-specific fields
Typical Tasks Develop and maintain data architectures, design and implement ETL processes, ensure data availability and quality Analyze data, create and validate models, interpret results, communicate findings to non-technical stakeholders
Career Path Can progress into roles such as Senior Data Engineer, Big Data Architect, or Data Engineering Manager Can advance into roles like Senior Data Scientist, Machine Learning Engineer, or Chief Data Scientist
Demand in India Increasing demand with the growth of data-driven decision-making and digital transformation High demand due to the increasing importance of leveraging data for strategic decision-making
Salary Range (approx.) INR 6-10 lakhs per annum (Entry-Level), INR 12-25 lakhs per annum (Experienced) INR 8-15 lakhs per annum (Entry-Level), INR 18-40 lakhs per annum (Experienced)

Job Role

Data Engineer Job Role:

The professionals in the data engineer job role,design, build, test, integrate, manage, and optimize data from different sources and create infrastructure using that data. The main motto of data engineers is to make a continuous flow of data by integrating several technologies. They also create composite queries to access the data easily. Mainly, the role of data engineers in a company is to manage the data pipeline of the system.

Data Scientist Job Role:

The main role of data scientists is to analyze the data infrastructure created by data engineers. They come froma Mathematics and statistical analytics background that makes them suitable for managing business operations and the market. Data scientists perform online experiments to create customized data products to improve business operations. They understand the needs of business leaders by connecting with them and represent their findings in a simpler way so that the normal business audience can understand that easily.

Educational Requirements:

In general, you can notice that computer science is the common educational background for both data scientist and data engineer posts. However, data scientists have knowledge of statistics, mathematics, operation research, and business field compared to data engineers. Here are the details of the educational requirements for both the job posts.

Educational Requirements for Data Engineer Job Post:

To work as a data engineer in a company, you need to have a bachelor’s degree in mathematics, computer science, or information technology. Along with the bachelor’s degree, it is good to have additional data engineering certifications to get this job. The following are the educational requirements to get the data engineer job.

  • Bachelor’s degree in statistics, computer science, information technology, or any other equivalent field
  • Experience in cloud-based data solutions such as AWS, EMR, RDS, Redshift, EC2, etc.
  • Experience with analytical tools, automation, configuration management, system monitoring, dashboarding, and analyzing the external and internal root cause
  • Five years of professional experience or three years of experience with a master’s degree
  • Knowledge of programming languages like Java, C++, Python, Scala, etc.
  • Knowledge of different databases and working experience in the relational database
  • Experience in creating, managing, and optimizing large data infrastructures and pipelines
  • Strong analytical and management skills to deal with unstructured data
  • Both writing and debugging knowledge of SQL

Educational Requirements for Data Scientist Job:

Mostly, companies prefer candidates with a Ph. D or master’s degree for the data scientist job. Apart from that, candidates with a degree in computer science, mathematics, and statistics can also apply for this job. Since data scientists work with various data sets and analyze them, they need to have knowledge in the data infrastructure, machine learning, statistics, etc. The following are all the things that you need to have to apply for this job.

  • D or master’s degree in mathematics, computer science, engineering, statistics, or any other equivalent field
  • Higher analytical and mathematical skills
  • Experience in data mining technologies, machine learning technologies, including neural networks, decision tree learning, clustering
  • Experience in working in cloud-based databases with large volumes of data
  • Knowledge of programming languages such as Scala, Java, MATLAB, C, SQL, Python, and R
  • Working experience in data science and analytical roles for five years or more
  • Experience in advanced statistics methods
  • Data analysis experience from third-party providers such as Facebook insights, google analytics, andAdWords
  • Knowledge of system integration and architecture
  • The capability of presenting technical terms to the non-technical audience in an understandable manner
  • Understanding of experimental design and A/B testing
  • Strong knowledge of predictive modeling algorithms and frameworks


As discussed above, both data engineers and data scientists work together in the data science field to improve the business operations of the company. Therefore, it can be said that both job posts are complemented of one another. The following are the responsibilities of these jobs that you need to know.

Responsibilities of Data Engineer:

I general, data engineers handle raw data sets that may contain machine, instrument, or human errors.They apply advanced methods to improve the quality and efficiency of data. Data engineers are mainly responsible for collating, designing, creating, and maintaining real-time data. They build the data infrastructure that is needed by data scientists. Also, they maintain large databases and systems in companies.

This way, data engineers work in various departments in companies. So, they need to have better communication skills to explain the technical terms to non-technical people efficiently.The following are the responsibilities of data engineers.

  • Build data infrastructure for analysis, transformation, and data loading from different sources like SQL technologies, AWS, etc.
  • Create data analytical tools for data pipelines
  • Engage with stakeholders in different departments to process business operations efficiently
  • Recognize, design, execute,and improve internal processing
  • Help data scientists in creating the data infrastructure and analyzing and optimizing data.
  • Make the manual processes automatic
  • Creating long term effects of the design decisions and making failure frame works
  • Design data quality frameworks, data integrations, and open source tools for data pipeline

Responsibilities of Data Scientists:

Data scientists mostly work with data sets that are formatted and validated by data engineers. Therefore, they can implement advanced analytical programs efficiently. Also, they create questions and get answers from the data sets that are fed into the system. Apart from that, they also explore and examine the hidden data to create better analytical reports. After analyzing the data, data scientists explain those results to the stakeholders on a monthly, weekly, or daily basis. Here are the responsibilities of data scientists.

  • Understand the needs of the company and provide new ideas to improve the products using new or the old data sets
  • Represent the analytical results to stakeholders and clients
  • Enhance and support the existing data science products
  • Design tools to monitor and analyzedata infrastructures and maintain data accuracy
  • Build custom data models and algorithms
  • Make use of proper databases and project designs to improve joint development efforts
  • Create ideas, statistical models, and perform experiments
  • Design data-driven solutions repeatedly to resolve critical business problems
  • Build an A/B testing framework and check the model quality


Both data engineers and data scientists get higher salaries and have a bright career ahead. Therefore, you can get a better working opportunity as per your skills and experiences,no matter which career option you choose.

Salary of A Data Engineer:

The salary of data engineers is decided according to the experience, role, and job location. However, the annual salary of a data engineer is around 9 lakhs.

Salary of A Data Scientist:

Like data engineers, data scientists also get their salary according to their qualifications, skills, experience, and job location. The salary of a data scientist can range from 7 lakh to 10 lakhs.

Bottom Line:

Now, you know the difference between data scientist and data engineer job roles and can decide the right career option for you. No matter which career path you select, you should have the required skills and work efficiently to improve your demand.

Leave a Comment

Scroll to Top