Data Engineer vs. Data Scientist

Data science is a growing industry in India and globally. Data assimilation is critical to business, industry, and government for everyday decision-making. The data collected helps a business reach its goals, a doctor to keep up with recent medical developments, an aviation mechanic repair aircraft, and a video gaming company determine what age group plays their games most often. 

As the field of data science grows, so will job opportunities. If you’re considering entering data science or data engineering, thoroughly understanding the necessary data scientist skills is an essential first step. This article will look at two primary roles in data science–the data engineer and the data scientist–and explore what sets them apart. It will also provide insight into data engineering and data scientist jobs. 

What is the role of a data scientist and data engineer? 

While the two roles overlap, each has distinct duties. Data scientists use their math, physics, and statistics backgrounds to discover useful information within data sets. They create models for artificial intelligence and machine learning, analyse data, and interact with an organisation's business side. As a data scientist, you will focus on gleaning insight and information from data. 

Data engineers typically have a programming background, often focusing on organizing and maintaining the infrastructure for big data. Like data scientists, data engineers often interact with the business side of an organisation, but the role focuses more on using big data to generate software-based solutions. In this role, you will use your technology expertise to extract, clean, and move data, transforming it to facilitate data scientists’ ability to perform their tasks.

Data engineer and data scientist differences

Tasks and responsibilities

Data science fields are fast-paced and require strong computing and communication skills. Although the roles of data scientist and data engineer fall under the umbrella of data science, their tasks and responsibilities differ, and they require the same analytical and methodological skills to work with data.

Data scientist

A data scientist works in various settings to clean and analyse data and provides metrics to solve problems using online experiments and other methods. They have skills in interpreting data to determine solutions, opportunities for improvement, and patterns and trends.

Data engineer 

A data engineer builds, tests, and maintains the tools necessary for data collection. The data scientist then uses it for analysis. The data engineer uses skills such as software engineering practices to prepare data for use.

Data engineer salary and job outlook

What can those data engineer skills you’ve built help you earn? On average, data engineer jobs have annual base wages of ₹9,58,057, according to Payscale [1]. Additional compensation may come in the form of bonuses, overtime pay, and any other benefits offered by an employer. 

While that can give you a general idea of what to expect, your experience level may impact your earnings. For example, an entry-level data engineer with less than one year of experience can expect to earn a total annual salary of ₹5,18,398. A data engineer with between one and four years of experience earns an average of ₹7,96,009, and an engineer with five to nine years in the field typically earns an average annual total wage of ₹15,75,280. With more than 10 years of experience, you might make an average of ₹22,36,175 in total compensation [1]. 

Analytics India’s report “State of Data Engineering in India 2024” shows the Indian data engineering market growing from 29.1 billion USD in 2023 to 124.7 billion USD in 2028 [2].

Data scientist salary and job outlook

According to Payscale, the average annual salary for a data scientist is ₹10,12,980 [3]. A data scientist with less than one year of experience can expect to earn an average pay of ₹5,95,255 including compensation. Those with one to four years of experience earn an average compensation of ₹9,85,349, and a data scientist with five to nine years of experience earns an average compensation of ₹17,41,069. A data scientist with over 10 years of experience can expect to earn an average compensation of ₹24,33,634 [3].

The Times of India reported that the analytics industry in India will reach 98 billion USD by 2025, increasing to almost 119 billion USD in 2026 [4]. India’s open data engineer jobs and demand for data scientists will remain high, with approximately 11 million data science jobs available in India alone by 2026 [4].

Skills for a data engineer

Data engineers are highly skilled in many areas of IT. They need scripting skills like Python, JavaScript, TypeScript, and PowerShell. Data engineers also need the following skills:

Programming languages: Cloud computing has changed some required languages, and a data engineer needs to keep pace with these changes. Java, C#, and C++ are still used, while Go, Ruby, Rust, and Python are increasingly popular cloud languages.

Database systems: Data engineers must be familiar with database systems like SQL and NoSQL. The role involves storing and retrieving database management systems (DBMS).

Data warehousing solutions: Data engineers work on vast volumes of data, and it’s important to be familiar with data warehousing solutions like MarkLogic and Oracle.

Machine learning: Machine learning isn’t always an essential skill, but if you understand how to use data for statistical analysis and data modelling, it can help you with your career as a data engineer.

Skills for a data scientist 

Some essential skills for data scientists are cloud computing and statistics. The cloud provides space to hold, retrieve, and share data relatively inexpensively, while statistics and probability will help you collect and interpret data. Other skills include:

Machine learning: Data scientists use machine learning to find patterns in data and gain valuable insights. Because it uses artificial intelligence, machine learning reduces human error and allows data scientists to analyse data in real-time.

Mathematics: Data scientists need strong mathematical skills, such as linear algebra, statistics, calculus, and vector models. Other skills may be needed, depending on the role. 

Data wrangling: Data scientists need skills to clean raw data and turn it into a usable format. Data wrangling allows the data scientist to send large quantities of data to analytical tools.

Data scientist career path

The majority of data scientists have a bachelor’s degree in data science or a closely related field. A master’s degree can make you more competitive and attractive to potential employers. However, certain skills, credentials, and Professional Certificates can add value and help you gain a job as well.

Data engineer career path

Data engineers typically begin their careers as software engineers or in a related field. A bachelor’s degree in computer science or a similar field is typically required, but you may qualify for a position if you possess the knowledge and skills. 

Which career is best for you?

If you enjoy coding and using the latest technology, data engineering could be the career you’re looking for. Conversely, if you’re curious and apt at gleaning insights from data, a career in data science could best suit you. 

If you want to explore a career in data engineering, the Introduction to Data Engineering certificate course offered by IBM on Coursera will teach you the core concepts and processes of data engineering. 

IBM also offers a Data Science Professional Certificate if you’re keen on a career in data science. You can take it online and complete it in approximately four months. 

With additional training, a data scientist can become a data engineer, and a data engineer can become a data scientist, so you aren’t locked into one or the other. Both roles have foundational skills in IT and data science.

Build your data skills on Coursera 

With the Data Engineering, Big Data, and Machine Learning on GCP Specialisation offered by Google Cloud on Coursera, you can gain experience in big data and machine learning. The IBM Data Warehouse Engineer Professional Certificate is a self-paced beginner-level Professional Certificate that can be completed in four months. 

If you’re keen on exploring more about data science, consider the Learn SQL Basics for Data Science Specialisation offered by the University of California, Davis on Coursera.