Evaluating IT Skills or Whether to Go With: Data Engineer vs Data Scientist

Business data could comprise any statistical data, unprocessed analytical data, consumer feedback data, sales figures, and other information. Regardless of how big or small your firm is, data is vital.

Effective problem-solving without correct data insights is almost impossible; business executives can identify and handle key issues as well as monitor the effects of suggested solutions with a set of data-based insights.

The fact is that the bigger businesses are the more operations with more data they can perform. Nevertheless, implementing analytics has benefits that go beyond scale; this also provides you with benefits in a competitive environment in today’s fast-moving industry.

You can choose where to concentrate your resources by determining which kind of data will bring the most value to your business. Companies need to know how to use this data-driven knowledge to make decisions about how to run their businesses. Data engineer and data scientist are the professionals they require for that.

Who’s who in the data engineer vs data scientist pair? To put it simply, data engineers gather pertinent data, deliver it to one place, and clean it up for the data consulting group. Whereas data science services include analysis, testing, aggregation, and optimization. 

We’ll learn more about each job position in this article.

Data Engineer

Using their expertise and the appropriate tools, data engineer specialist collects and handles data on a massive scale, making sure that it is usable and correct. They create distributed systems that gather, manage, and transform unprocessed data into information that can be used by data science companies, machine learning specialists and business intelligence professionals in a variety of applications.

The primary skills and duties of data engineering are briefly described below:

  1. Data modeling and architecture design

This entails deciding how data will be stored, arranged, and retrieved as well as establishing its structure. Data engineers need to be able to translate organizational needs into a suitable data model by comprehending the business requirements. They should be skilled with various data modeling strategies and have database design knowledge.

  1. Database management and optimization

Database management and performance, scalability, and reliability optimization are other areas of data consultancy services. They should be proficient in relational and columnar datasets as well as have a thorough understanding of database technology. Also, they need to be skilled in the database management system and problem resolution.

  1. ETL (extract, transform, load) processes

ETL processes, which move data from numerous sources into the company’s data warehouse or datastore, are created and maintained by data engineers. In order to do this, data must be gathered from various sources, modified to meet the data model, and then loaded into the right database. Data engineers should be knowledgeable with ETL frameworks and tools and have hands-on integration of data experience.

To perform this kind of job, data engineers also need to have knowledge of coding languages, like Python or Java; experience with cloud services, proficiency in data security; experience with Apache Hadoop-Based Analytics (discover more about hadoop consulting in our previous article) and knowledge of operating systems like UNIX, Linux, Solaris.

Advantages of Hiring a Data Engineer

If you want to build apps or data platforms (data lakes, data warehouses) that require you to extract and combine data from numerous sources, you should hire a data engineer. A qualified specialist will bring your project:

  1. Expertise in designing and managing data infrastructure

This entails creating ETL methods to transfer data between systems as well as designing and constructing databases, data stores, and data lakes.

  1. Focus on ensuring data accuracy and consistency

To guarantee that data is gathered correctly within the company, data engineers create governance structures and processes to validate and clean up data. Data quality should be a high priority for any business.

  1. Strong technical skills in database management and optimization. 

Professionals should know programming languages, deal with cloud platforms, transactional databases, and data structures, and utilize tools and systems like Kafka, Spark, etc.

Disadvantages of Hiring a Data Engineer

Overall, the biggest disadvantage is mainly caused by their advantage — they’re technical specialists. This means, they know how to get clean data but can not give business insights from it. Therefore, with a data engineer, companies can get such drawbacks:

  1. Insufficient knowledge of advanced data analysis and machine learning
  2. Little understanding of business needs and goals
  3. Potentially higher salary requirements

But these pitfalls can easily be solved along with a competent data scientist or the data science consulting companies.

Data Scientist

A data scientist works with data from diverse sources and evaluates it to gain a comprehensive picture of how business functions and what choices should be made. The data consulting companies combine in their work AI, analytic, and statistical tools to improve certain business procedures and determine smart solutions to challenges the business faces.

Main duties and skills include:

  1. Advanced statistical analysis and machine learning

Data scientists need a thorough understanding of statistical analysis and machine learning methods to create prediction models, find patterns, and spot trends both positive and negative. Also, a specialist need to be well versed in statistical modeling methods, like regression, clustering, and classification, as well as utilize languages R or Python, and deep learning methods such as recurrent, and neural networks.

  1. Predictive modeling and data visualization

To create predictive models, data scientists use data visualization tools that aid in sharing insights with stakeholders. Therefore, they should have expertise in building interactive dashboards to present the needed data and provide ideas with data visualization tools like Tableau, Power BI, and D3.js.

  1. Understanding of business goals and objectives

Understanding key performance indicators and metrics, as well as the business procedures and workflows that are essential to the running of the organization, are all essential parts of data science consultancy. They need to know how to convert business inquiries into information-driven insights that can aid in decision-making.

Advantages of Hiring a Data Scientist

Any firm can benefit greatly from hiring a qualified data scientist:

  1. Expertise in analyzing and interpreting complex data sets

Large and complicated datasets might contain patterns, trends, and insights that are difficult for the unaided eye to spot. Data scientists are experts at finding these things. They interpret the data using sophisticated statistical and mathematical tools, allowing firms to make wise judgments.

  1. Ability to develop predictive models and insights to drive business decisions

Data scientists not only just analyze data and shows the results of, for example, past campaigns; they can predict future events based on historical data by utilizing machine learning algorithms and other predictive modeling approaches. This makes it possible for organizations to foresee trends, find opportunities, and make data-driven choices that will increase their bottom line.

  1. Understanding of the wider business context and goals

They are aware that data analysis involves more than just crunching numbers; it also entails comprehending corporate goals, target audiences, and market trends. Data scientists work with other teams to match their data insights with organizational objectives, assisting organizations in making strategic decisions that are in line with their long-term aims.

Disadvantages of Hiring a Data Scientist

Likewise, in the case of a data engineer, a data scientist’s main advantage is a reason for the disadvantage — being not very tech-savvy. 

  1. Limited expertise in designing and managing data infrastructure

Data scientists usually are not very experienced with activities like data gathering, storing, and processing, as well as creating databases and data pipelines. In order to make sure that their data architecture is up to pace, businesses may need to cooperate with outsource development companies or comply with their data science team with additional resources.

  1. Potentially less focus on data accuracy and consistency

Even though data scientists specialize in finding patterns and insights in data, they still may not always give accuracy and consistency of data, like fully equipped research and development teams, due to the fact that they place a greater emphasis on data insight extraction than on ensuring the data is correct across sources. This may result in data mistakes or inconsistencies, which may affect the correctness of the decisions drawn from them.

  1. Potentially higher salary requirements

This is a common disadvantage but can be minimized in different ways, for example, by outsourcing jobs to data science consulting firms or hiring a remote data scientist consultant. 


Depending on what project goals you are trying to achieve, you can choose the appropriate professional: if you need someone with a more technical background to create data warehouses and clean up the masses of data, Data Engineers are your choice; if you already have collected the needed data in one place and need a bright mind to transform it into business development ideas, look for Data Scientists. Perhaps, you might even need both if no data work has ever been done. Every option is possible, just make up your mind on the budget, project scope, and business needs. 


Emma Kostyukovic

Emma Kostyukovic is a driven and knowledgeable Information technology expert. She serves as a Content Manager at Squadrity, info portal about top IT companies in Ukraine in various categories. Emma is current on the new trends in the areas of software, agile project management and product growth hacking. She regularly posts her insights online to assist tech startups and companies in staying up-to-date.