A Comparison Between Data Scientists and Engineers

Data Analysts, Data Engineers, Data Scientists, Applied Scientists and Machine Learning Engineers are key roles in ensuring the success of contemporary digital business data teams. Data Teams liaise with stakeholders from multiple departments to construct predictive analytics and machine learning-based data solutions.

Downstream machine learning applications are dependent on an organization’s data architecture and Extract, Transform and Load (ETL) operations. As a result, the need for data engineers has increased in line with the number of companies beginning AI and digital transformation projects. Data engineers are responsible for creating the data infrastructure and pipelines, as well as providing data scientists with convenient access to the processed data they require to create machine learning models.

In this article, we will be looking at the various aspects of the roles of a Data Engineer and a Data Scientist, including their duties and responsibilities, educational qualifications, areas of expertise and opportunities for career progression. We will be comparing and contrasting the two roles to gain a better understanding of the differences between them.

Functions of Data Scientists and Data Engineers

Data Engineers are responsible for creating the pipeline infrastructure that Data Scientists use to input models for a range of purposes. For this reason, Data Engineers are often employed before Data Scientists to ensure the data platform is in place. In smaller businesses and startups, personnel may specialize in both Data Engineering and Data Science. To expand a company’s data science capabilities, dedicated Data Engineering and Data Science staff are essential.

Tasks assigned to a data engineer

  • Construct and keep up data processing systems on a grand scale
  • Possess a thorough familiarity with a broad range of data integration, transformation, and deployment tools
  • Boost data efficiency by enhancing information flow.
  • Figure out how to get your hands on fresh data and incorporate it into your workflow.
  • Establish mining and processing procedures
  • Create own software or use existing solutions to join the data ecosystem.
  • Store information

The duties of a data scientist

  • To determine the full nature of the issue and the data that will be required, an exploratory data analysis should be performed.
  • Apply sophisticated statistical methods to massive data sets in order to spot patterns and trends
  • Incorporate data science into the creation of algorithms and prediction models for practical commercial applications.
  • Streamline data science operations, beginning with data collection and ending with deployment.
  • Share findings with stakeholders and work together to create data products.

It is commonplace for Data Engineers to respond to requests for cleaned and processed data from Data Scientists on a daily basis, as well as writing code, constructing data pipelines, and managing various facets of the data infrastructure. The bulk of a Data Scientist’s time is spent crafting and training machine learning models, carrying out multiple experiments to enhance the model’s efficacy, and engaging with stakeholders from various departments, such as Engineering, Product, and Business, to analyze findings and develop new applications.

Data engineers and data scientists are trained differently.

Typically, a data engineer should possess a Bachelor’s degree in either Computer Science or Information Technology. Their expertise encompasses software engineering, such as coding, algorithm design, data structure creation, system architecture and software tool development. As cloud computing increasingly becomes a fundamental element of digital businesses, data engineers should be knowledgeable in the major cloud platforms (e.g. Amazon Web Services, Microsoft Azure and Google Cloud Platform) and the cloud technologies (data warehousing, data visualization, and data analytics) that operate on them.

The use of cloud-based machine learning services and APIs is commonplace for typical applications such as recommender systems, computer vision and natural language processing, to allow data scientists to save time and effort over developing their own solutions from scratch. During the recruitment process for data scientists and data engineers, certification from these cloud providers is often a requirement.

In order to interact effectively with the data team, engineers must have a strong understanding of statistics and/or machine learning, as data engineering focuses on creating data platforms for data scientists.

Data Scientists typically come from a range of academic backgrounds, having completed undergraduate study in areas such as Computer Science, Statistics, Mathematics, Physics, Psychology and Biology. Generally, a Data Scientist will hold a postgraduate degree (Master’s or Doctoral) in one of the aforementioned disciplines. However, in recent years, it has become increasingly common for entry-level Data Science roles to not require such qualifications.

Data Scientists are required to collaborate with many stakeholders from engineering, analytics, product, and business teams. Therefore, it is advantageous for them to have an understanding of those areas. In order to help cross-functional teams create a successful, collaborative data product, Data Scientists need to possess strong communication and narrative skills.


As demand for data scientists and engineers increases, there is an ever-growing need for up-to-date, hands-on training. To meet this need, many leading technology firms, such as Google, Microsoft, Amazon Web Services, IBM, and others, are offering certifications tailored to specific industries. By completing an accredited course, candidates can demonstrate their aptitude and skill set to prospective employers.

A good data engineer may have one or more of the following certifications or areas of expertise:

  • Validation as a Google Cloud Data Engineer
  • Specialist Certification in Microsoft Azure Data Engineering Associate
  • Certification as an IBM Data Engineer
  • IBM’s Foundations of Data Engineering Course

Some examples of certifications or areas of expertise that a good data scientist could have are listed below.

  • Deep Learning in Artificial Intelligence
  • A DataCamp Specialization for Python-Useful Data Scientists
  • Workshop on Data Science at Flatiron
  • The General Assembly’s Deep Dive Into Data Science Bootcamp

For those aspiring to become data engineers or scientists, it is important to consider their available resources, time and interests when choosing the most suitable path for them. Rather than attempting to attend as many classes as possible, it is more beneficial to focus on those which are most likely to enhance their data engineering or data science skillset.

Data engineers and data scientists have different career paths.

Data Scientists and Engineers are both likely to have a positive job outlook. A Data Engineer’s career path may lead them to roles such as a Data Architect or Solutions Architect. They may then progress into more traditional technical leadership roles or take on more creative duties, such as envisioning and leading teams on data platforms. Data Engineers may also transition into Data Scientists with a better understanding of core Data Science skills, such as Statistics and Machine Learning.

For more than a decade, the demand for data scientists has steadily grown. There are now plentiful opportunities available to new graduates in businesses of all sizes and across a range of industries. Thanks to the development of tools and technologies that simplify and automate the data science lifecycle, data science is no longer the exclusive domain of professionals with deep domain understanding and PhDs. Depending on their goals, data scientists can either become renowned domain experts as individual contributors or set up data science teams and organizations as data science leaders. Alternatively, they can pivot laterally into data engineering or machine learning engineering roles, by developing their knowledge of software engineering principles such as data structures, algorithms and optimized code.

In conclusion

Companies are increasingly recognizing the importance of data science for business success, and are consequently expanding their data science teams and capabilities. Data engineers form the foundation of data storage and processing, and are responsible for the infrastructure necessary for data scientists to create machine learning models and applications, such as data warehouses and pipelines.

It’s time to lay the groundwork for a solid data infrastructure at your firm.

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