Thanks to the growth of digital and cloud-based technologies, we are in an era that is driven by data. Due to this, when you make a quick search on any job portal, you will realise that data scientists and data engineers are some of the in-demand career opportunities across the world. According to the Bureau of Labour Statistics report, the positions for data scientists will increase by 16% between the years 2018 and 2028. Further, Glassdoor’s survey ranked the data positions, including data scientist and data engineer, in the list of top 50 best jobs.
However, there is a common misconception among people that both data engineer and data scientist roles are similar, which is not true at all. If you are confused about what to choose between the two, understanding the difference between data engineer and data scientist will help you make an informed decision about your career. Being one of the most ambiguous fields in the tech world, the positions, roles, and responsibilities for both designations are still maturing.
Here we will share in-depth insights, including skill sets, career opportunities, roles, salary, and outcomes, helping you to gain a clear view and draw a line between these two tangible roles.
Data Science is an interdisciplinary subject involving the methods and tools from the statistical, application domain, and computer science to process structured and unstructured data to gain meaningful insights and knowledge.
A data scientist dives into the data and works on building and executing AI-based algorithms in various business verticals to solve complex issues. Furthermore, they also perform data visualisation and dashboarding mechanisms to identify trends and patterns in the industry.
READ MORE: What is Data Science?
Data Engineering is the process of designing and building a process stack for collecting, storing, enriching, and processing data with real-time authentication. The discipline usually employs tools and programming languages to build API for large-scale processing data and query optimisation.
Data Engineers are involved in taking care of hardware and software requirements of the organisation while also focusing on IT and Data protection and compliance with the security policies. They are also responsible for ensuring the continuous follow of data from the servest o the applications.
According to a recent LinkedIn report, data science and data engineering are listed among the top 15 in-demand jobs, and their statistics speak for the demand and growth of these roles. Let us take a look at an overview of these vocations.
The paradigm of data scientist and data engineer roadmap takes place at the same point. However, since big data continues to grow and evolve, it will raise new functional and specialisation. Even though both roles pursue insights through accurate data analysis, they get separated in their roles and responsibilities in achieving the desired results.
Data scientists primarily work with a huge amount of data in analysing, processing, and modelling it to produce meaningful information. It will help solve problems or aid in the decision-making process within business or project needs.
The responsibilities of data scientists include –
ALSO READ: Why you should become a data scientist
Data engineers are responsible for setting the table for data science. They have to lay the solid groundwork so the data scientist can work on their craft, fulfilling the organisation’s requirements. They create the systems, infrastructure, and architecture required to obtain, store, produce and prepare raw data to be utilised by data scientists to carry out their tasks.
Data Engineer’s roles and responsibilities are listed out below –
Big data skills are critical to landing a job in data engineering and data science. From designing and maintaining data to learning about big data frameworks, one needs to acquire important skills to build a successful career in the field.
There are several specialised tools and skills required for data scientists to enable them to spot trends and make better-informed predictions. Some of them are –
R and Python are the most popular data science programming languages, well-suited for data analysis operations.
Machine learning tools such as TensorFlow, Apache Mahout, and Accord.Net will help you to apply artificial intelligence to be more accurate in analytical models.
Visualisation tools such as SQL and using programs like Bokeh, Plotly, and Tableau will help you to present complex data in a wide array of charts and graphs.
Data scientists should be proficient in applying statistical concepts and techniques that will help them to analyse and work with the data to achieve better results.
Below are some of the essential data engineer skills and tools that will help you to tackle the challenges in the ever-growing pile of big data that every business verticals face today.
Having in-depth knowledge of SQL and NoSQL will help one to collect, store, and query information from databases in real-time.
Data transformation can be either simple or complex, depending on the data sources, formats and desired output. Some of the relevant tools are Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage and more.
Data warehouse along with ETL aids you to leverage big data in a meaningful way. Some of the popular tools like AWS Glue, Stitch, and Informatica PowerCenter will streamline data that comes from heterogeneous sources
Setting up the cloud to store and assure the high availability is one of the primary tasks of data engineers. Therefore, acquiring knowledge on the cloud platforms like Azure, ACP, OpenStack, and OpenShift is important.
A recent poll has revealed that there is a need for 2,00,000 Data Engineers and Data Scientists in the next five years. Read to learn more about the career pathways and how to build a bright future in data science and data engineering.
A successful data scientist must have a perfect combination of all aspects, from being a programmer to a mathematician. In addition, the field is constantly evolving to give more than one type of job role and title. Here are some different jobs under the data science umbrella,
The individual has to offer the best solution to challenges using data analysis and processing and help make a better decision.
The data analyst performs various tasks, including visualisation, munging, and processing massive amounts of data. They also have to create and modify algorithms to cull information from the biggest databases without corrupting the source.
The job description of a database administrator is to ensure the proper functioning of all databases of the organisation and revoke the services depending on the needs. Moreover, they are also responsible for backups and recoveries of databases.
A blend of software engineering and data science, the professional has to deal with big data. They need to perform A/B testing, build data pipelines and implement common ML algorithms such as classification.
There are numerous aspects of careers in data engineering. From coding the algorithms to the developing side of things, they take on various job roles, including –
Data Engineers have to build and test scalable Big Data ecosystems. They also have to update the existing system with an upgraded version to enhance the efficiency of the databases.
They are responsible for developing data pipeline infrastructure. They also save data from various sources, such as steaming or social media sources to create collection processes, gathering the data.
Administrators are responsible for testing, designing, and maintaining database systems used to store collected data. They have to optimise them for more secure and efficient operations, ensuring a smooth collection and storage process.
They implement programming languages such as Python and databases like SQL and NoSQL to have better control over the processing systems. They ensure to run the database smoothly, searching for various ways to optimise the process.
Data scientist and Data Engineer are both lucrative jobs with certain similar skills and experience related to one another. However, due to some differences, including the roles and responsibilities, there will be significant differences in salary. Given that, let us compare the salary for these from the real data derived from Glassdoor.
The average data scientist’s salary in India is approximately INR 11,00,000 per annum. In addition to the given remuneration, the number can vary depending on the title, such as Entry level Data Scientist, Data Scientist, Senior Data Scientist, Lead Data Scientist, Data Science Manager, and Data Science Director.
The average salary for a Data Engineer is around INR 8,00,000 per annum in India, which might vary depending on various factors, including job location, skills, and experience. In addition, the title will also make some significant salary changes as Data Engineer, Senior Data Engineer, Data Engineering Manager, Lead Software Engineer, and Data Scientist.
The difference in the salary is because the data engineers are less popular cousins of data scientists but no less important than the latter. In a nutshell, a career in either data science or data engineering is a most sought-after and fulfilling one.
To work as a data scientist in the booming industry, you must meet certain eligibility requirements. Here are some steps to add strength to your candidate profile among the recruiters.
Data scientists must have an undergraduate or postgraduate degree in a relevant discipline in Business Information systems, computer science, Economics, Information Management, Mathematics, and Statistics. Moreover, earning an MBA in Data Science is more oriented toward becoming a potential professional.
Take up online courses relevant to data science that will teach you essential skills such as Statistics, Probability, Linear Algebra, Bioinformatics, speech processing, NLP, computer vision, and more.
Any prior experience in the field as a professional or intern will lure high packages and top recruiters. It also helps you to hone your high-level analytics, programming, computing, and visualising skills.
ALSO READ: Why you should become a data scientist
Certification alone isn’t sufficient to gain a job in the data engineering field. Proper education and experience are also necessary to become a potential data engineer. Here are some steps,
A relevant academic degree in applied mathematics, computer science, physics, or engineering. Furthermore, a master’s degree in computer science or computer engineering will also set you apart from the competition.
Online courses are an excellent way to obtain critical data engineering skills. There are multiple niches of resources and courses that cover the fundamental concepts for beginners and even consistently get updated for professionals.
Get hands-on experience to gain more practice knowledge on data engineering skills. The first step is to make a project goal and determine the steps to reach it. It will aid in maintaining motivation, structure learning, and business intelligence.
ALSO READ: Why you should become an data engineer
As a huge amount of data reshapes the industrial landscape of the current century, new roles in data positions are constantly emerging. Are you interested in helping businesses discover solutions to their problems with data? Does the concept of unveiling valuable data insights with technological advancements inspire your career move? Enrol in the Master’s Programme in Data Science provided by the Manipal Academy of Higher Education (MAHE) with Online Manipal.
These courses are designed to help aspiring individuals gain a high level of competitive intelligence and high levels of confidence in assessing and managing tasks involving a large set of data produced using innovative technologies. We help you to upskill yourself to experience an interesting and high-paid career in the most demanding field. It’s never late to check out the Data Science course by Online Manipal.
Big data is changing the world, and creating a data-driven culture has become inevitable. But do companies prefer one role over the other? Both Data scientists and data engineers play a crucial role in maintaining the long-term and efficient data infrastructure of an organisation. Despite the difference, their roles are interdependent, complementing each other and aiding in shaping the ever-expanding resources.
Simply put, the data engineer acts as the ‘Architect’ of the data while the data scientist is the ‘builder’ of the plan made by the architect. Whichever path you choose, both jobs will continue to be in high demand through the foreseeable future of the data-driven, digital world. Explore the plethora of courses on Online Manipal today!
Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.
Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.
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