Data Engineer vs Data Scientist

It’s no hype that companies are planning to adopt digital transformation in the recent future. Due to digital transformation, companies are being compelled to change their business approach and accept the new reality. There are already many services, which were traditionally done manually, that are being offered online and companies are embracing innovative techniques to give an unmatched end-user experience. For achieving success with digital initiatives, companies need professionals that are skilled in Big Data analytics and data science.

Where Big Data refers to the massive amount of data being generated every day in different formats, data science is all about gathering, cleaning, analyzing, modeling, and visualizing that data to draw meaningful insights and make more informed decisions. Professionals skilled in any of these domains are in high demand for companies taking digital initiatives. Two of the popular job roles that have gained a lot of traction over the past few years are data engineer and data scientist. As both the fields are relatively new, it is easy for beginners to get confused between the two terms.

This article explains to you what exactly data engineers and data scientists refer to and why achieving a Data Engineering certification or data science certification is worth your time and effort.

What is a Data Scientist?

Data scientists are professionals who are capable of creating machine learning-based tools or processes within a company. They use advanced levels of data techniques like neural networks, regression, clustering, decision trees, and more to draw meaningful business outcomes. In this process, they usually collaborate with data engineers and data analysts to take data inputs and formulate actionable insights that are later shared with the stakeholders to enhance their decision making.

Some of the responsibilities handled by data scientists include:

  • Gathering, merging, analyzing, and visualizing data to uncover hidden trends or patterns
  • Use tools like Python, R Tableau, SQL, Impala, Excel, or more to handle data
  • Design and test new algorithms to simplify data problems
  • Communicate with various stakeholders to explain to them the insights found and how will it impact their decisions and align with the business needs
  • Constantly updating with technological changes and incorporate them if they are useful in solving the data problems more effectively
  • Applying techniques like statistical analysis, data modeling, and machine learning on data and measure the results at the end

Let us next know about a data engineer.

What is a Data Engineer?

Data engineers are professionals responsible for planning, designing, and maintaining data architectures according to the business needs of an organization. Their task begins with looking at the various requirements and applying appropriate database techniques to build a robust architecture. Next, they start with the implementation process to develop the database from scratch. Such professionals also assist the data science team by creating dataset procedures used during data mining, modeling, and production.

Some of the responsibilities of a data engineer include:

  • Perform data cleaning, aggregation, and organization from disparate data sources and transfer it to a data warehouse.
  • Create data pipelines that convert raw data into formats that can be used for analysis.
  • Build and maintain the analytics infrastructure including servers, databases, and large-scale processing systems.
  • Build data set processes and look for ways to enhance data quality, reliability, and efficiency.
  • Clearly understand the business goals and conduct research to deal with any unexpected business problem.

Data Engineer vs. Data Scientist

Looking at the above information, you must have got some idea regarding how the two data roles differ. Here’s some more detailed information to help you draw a clear comparison between the two.

Skills required: Data scientists must be skilled in any one programming language like Python or R, frameworks like Spark and Hadoop, and technologies like machine learning and deep learning. Data engineers must be skilled in using programming languages, especially SQL, and focus more on Big Data frameworks like Hadoop, Apache Spark, NoSQL, Hive, Pig, and so on.

Salary prospects: Both data scientists and data engineers earn lucrative salaries. Taking the data from Payscale, a data scientist earns an average annual salary of $96,158 in the US. The salary figure for that of a data engineer stands at $92,298 per year.

Focus areas: The data scientists focus more on designing models to work on Big Data, interpret the analysis, and deliver outcomes that have a direct impact on business decisions. On the other hand, data engineers create data warehouses and work on the data before it is handed over for analysis. It means they focus on writing queries on data, maintain the design and architecture of data, and transform the raw data into a suitable form for conducting the analysis.

Tools used: Data scientists use tools like Python, R SAS, Scikit-Learn, MS Excel, Rapidminer, and Tableau. Data engineers mostly use tools like MOngoDB, SAP, Cassandra, Oracle. PostgreSQL, Hive, and Apache Spark. Some languages used are common to both the roles like C#, Java and Scala.

Advancement path: Professionals who start their career as a junior data engineer can aim for more ambitious roles like senior data engineer, lead data engineer, head of data engineering, and so on. Conversely, those who start working as a junior data scientist can take their career ahead with designations like senior data scientists, machine learning engineers, principal data scientists, and data architects.

Final Words

We hope that now you are able to differentiate between a data analyst and data engineer role. What you choose for your career depends entirely on your career interests. Companies are seeking skilled professionals for both the job roles and you need not worry about their demand in the future. Either way, your first step should be to gain the right skills by enrolling in an online training program. There are many reputed data engineering courses as well as data science courses that make you more employable and credible to handle data-related tasks. So, explore your options and step into the data world today.