Let’s discuss the key differences between data scientists and data engineers, primarily focusing on roles and responsibilities, educational qualifications, tools, languages and software, pay structure, job perspective etc.
The role of a data engineer has moderately gained momentum along with a data scientist.
Data Scientists’ Responsibilities
Generally speaking, data scientists arrange for cleaning, maneuvering and organizing big data. They use statistical measures and machine learning programs to assemble data used in prognostic modeling. They conduct industry research and rigorous data analysis to answer business requirements effectively.
Data Engineers’ Responsibilities
The data engineer is a person who fosters, builds, investigates and supports architectural databases and maintains enormous processing systems. Data engineers handle raw data that might consist of human, machine or instrument errors. They should recommend solutions and implement ways to improve data dependability, efficiency, and quality.
Apparently, both parties are essential to crunch the data and provide valuable insights for making crucial business decisions. Considerably, the efforts of both parties are imperative to design and present the data differently in the most functional manner.
Educational Qualifications
One basic similarity between a data scientist and a data engineer is their Computer Science backgrounds. Although data scientists are more likely to have studied statistics, operations research, economics, and mathematics. They often have an edge over data engineers on certain operational and business acumen techniques. On the contrary, data engineers typically come from computer engineering backgrounds only.
Tools, Languages & Software
Both the parties hold different levels of expertise in terms of tools, languages, and software. Very often, we observe that data engineers use tools such as SAP, Oracle, Cassandra, MySQL, Hive, MongoDB etc. On the other hand, data scientists use popular tools like Python and R for making data visualization and may also use packages as in Scikit-Learn, NumPy etc. for working on data science projects. It may interest you to know that a data scientist’s toolkit may also comprise of tools like Tableau, Rapidminer, Matlab, Excel, Gephi etc. However, Scala is more favored by data engineers as they can unify it with Spark to set up huge ETL flows. We may conclude saying that both the parties use few common Big Data technologies for their respective work areas.
Pay Structure
As per Paysa website; in the medium market, a data scientist makes an average of $100,000 on a yearly basis. On the other hand, a data engineer’s paycheck is considerably lower, i.e., up to $95000 per year. Although, it is quite unclear why there is a difference in the paycheck. It is interesting to know that there are more requirements for data scientists in the market than their counterparts.
Job Outlook and Perspective
Nowadays companies are resorting to flexible, measurable and reasonable solutions to manage and resolve data management issues. They are focused on migrating their data to Cloud so that they can use these data warehouses for future reference. Currently, the number of data engineers hired have considerably increased over the years. Companies are hiring for data science teams comprising of both data engineers and data scientists. Notably, the role of a data scientist has always been in demand. Organizations in the current generation prefer data scientists who possess good communication skills, artistry, technical expertise, ingenuity etc. Hence, recruiters are having a tough time to hiring the right pool of candidates as demand has clearly eclipsed supply in the market.
Artilcle Credit – Data Scientist versus Data Engineer: How are they different?