Data Engineering and Data Science: Decoding the “Data” Job Roles

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Ever since data became the new oil of the 21st century, big data job titles have branched out at an explosive rate. Anybody interested in working with big data can explore scads of opportunities. But first, let’s consider and understand the field’s most important terminologies – data science and data engineering.

While people often use both these terms interchangeably, they aren’t quite the same thing. Even though data scientist was lauded to be “sexiest job of the 21st century,” data engineer isn’t falling short. In fact, both the job roles are two peas in a pod. 

Read on to how these two promising job titles in the data realm differ and what it means for you. 


Data Science and Data Engineering: Responsibilities

Data science engineering includes managing raw data – structured or semi-structured- from machines, applications, and systems. The data might not be substantiated and can reflect abnormalities, such as system-specific field values or missing records. As such, big data engineers advise and execute ways to enhance data deliverability, reliability, quality, and efficiency to make data sets ready for data science usage. Moreover, certified data engineers create channels to reliably transfer data for other use cases, including data warehouse ingestion, data migration, and application integration.


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Data scientists gather data that has cleared the “first stage” of cleaning and manipulation from data engineering, which they later utilize to feed their machine learning (ML) projects, analytics applications, and statistical predictive models. That said, data scientists also leverage their pipelines to enhance that data with demographic insights, industry research, and behavioral information to address immediate business queries.


Data Science and Data Engineering: Technical Know-how

To fulfill the responsibilities discussed above, big data engineers and data scientists need to boast a solid skillset. Both these data pros must have advanced technical expertise. That said, the exact contents of this technical expertise might differ between the two roles.

The technical skills of a data scientist include:

Mathematics: This incorporates multivariate calculus, probability & statistics, linear algebra, and other fields based on the task.

Analytics and visualization: Data scientists scrutinize the analyses’ outcomes to understand the success of each experiment. Here, visualization and reporting play a crucial role in individual understanding and interacting with others in the organization.

Programming: Data scientists specialize in at least one big data framework or libraries, such as Python’s OpenCV for computer vision, scikit-learn for general ML, or PyTorch for deep neural networks (DNN).

AI and ML: Data scientists need to be well-versed with AI and ML and refine these theoretical models to boost performance and compare the subsequent outcomes of these models to pick the best option.

The technical skills of a certified data engineer include:

Distributed systems: To deal with massive data chunks, big data engineers must be strong at distributed systems, which contain various machines to boost their processing capacity and enhance their availability.

Programming: Exceptional programming skills are a must-have for any individual who has completed the data engineering course. Some of the most prevalent programming languages for big data include R, Java, Scala, and Python.

Databases, data lakes, and data warehouses: Data science engineering pros need to be aware of databases (relational and non-relational), data warehouses, and data lakes (reservoirs of unstructured data). Structured query language (SQL) is a critical data engineering tool, alongside technologies, including SOAP, HTTP, and REST, which help link these systems.

ETL and data integration: Certified data engineers must have a knack at extracting, transforming, loading (ETL) – pulling out data from several sources and combining it within a centralized site, including a data warehouse, for further inspection.  


Data Science and Data Engineering: Pay Scale

Learning data engineering and data science will both offer white-collar jobs. But, of course, like most other jobs, their pay scale relies on factors, such as location, experience, education level, company size, and industry.

Companies like IBM, Amazon, Infosys, and TCS, to name a few, are always on the hunt for both these data professionals.

If you land a job as a data scientist in India, you can make around ₹8.25 lakhs per annum on average, while after learning data engineering, you can land a job with an average income of ₹8.33 lakhs per annum.


Data Engineering and Data Science: Both the Jobs Complement Each Other

While we can argue that the “data scientist bubble” is likely to burst, we can’t deny that the demand for data expertise is robust, with an optimistic outlook for the near future. We need to acknowledge that both certified data engineer and data scientist are two complementary roles. Organizations leveraging big data must have experts in their arsenal with both the skill sets to utilize data’s true capabilities. Furthermore, you will have better odds anyway whether you pick a data science or data engineering course.


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Running through data engineering concepts without guidance is really frustrating, unmotivating, and confusing. Miles Education is here to help! Designed by IIT Jodhpur, along with Wiley Innovation Advisory Council, the program in data engineering and cloud computing ingrains you with all the technical skills essential to becoming job-ready data engineers. This 12-month intensive program is ideal for those already in the data field looking to hone their skills and take their career up by a notch.

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