Data Engineer vs Data Scientist

What Does a Data Engineer Do?

• A Data Engineer is responsible for developing data infrastructure for further data analysis. Data Engineers deal with raw data that is unformatted and contains human or machine-generated errors. They design, build and develop data pipelines to prepare the data by formatting and cleaning it.

They employ various big data technologies and advanced programming languages such as Java, Scala, Python, etc. to create these data pipelines. This prepared or cleaned data is further used by Data Scientists or Data Analysts to derive valuable insights. Data Engineers also need to ensure that built data pipelines are production-ready, robust, resilient, scalable, and secure.

What Does a Data Scientist Do?

• Data Scientists often work with the data prepared and generated by Data Engineers to extract actionable insights to drive decision-making processes in the organizations. They employ advanced analytical techniques such as Machine Learning, Deep Learning, Statistics, etc. on large amounts of data to process it and build predictive or prescriptive models using programming languages such as Python, R, etc.

Let’s have a look at the below diagram to understand how these two roles are interconnected with each other. This diagram depicts the Data Science hierarchy of needs or it can also be called as Hierarchy of Data Proces -

The above pyramid represents various steps a data can go through in organizations and at each step or level what kind of data professional they require. The first step is the collection of data from sensors, loggings, users, etc. After this step, Data Engineers work on collected data to build ETL pipelines in which data is formatted, and made usable and accessible to all stakeholders.

Then Data Scientists pick this data for further processing by applying advanced analytical techniques using various programming languages. As it is evident that Data Engineers are enablers for Data Scientists which makes these two roles heavily interconnected.

Data Scientists will collect this data using languages such as SQL etc., clean and prepare it, and develop a predictive model using Machine Learning algorithms.

Data Engineer vs. Data Scientist – Education

Data Engineers typically hold a bachelor’s degree in computer science, information technology, etc., or related fields. While Data Scientists generally have a master’s degree or Ph.D. in computer science, engineering, statistics, data science, economics, or closely related fields.

Though having a master’s or advanced degree is not a mandatory requirement to build a career in the Data Scientist job as long as you have the right set of technical skills required to perform the Data Scientist’s job.

Data Engineer vs. Data Scientist – Skills

Below Venn diagram shows that Data Engineer requires skills in software engineering and statistics and mathematics while Data Scientists also need to have great communications skills along with knowledge of software engineering and statistics.

We have created the below table to compare the skills required for both job profiles

If you think you lack some of the skills mentioned above, check out the Scaler’s Data Science program to upskill!

Data Engineer vs. Data Scientist – Job Responsibilities

Data Engineers are responsible for developing scalable data infrastructure that enables data to be accessible to each stakeholder for further analysis by building resilient and secure ETL data pipelines.

Their typical job responsibilities involve -

• Data collection from raw data sources based on business requirements
• Transform data into a format that is easy to use for further analysis by applying advanced programming languages
• Designing, building, evaluating, and maintaining resilient and production-ready ETL database pipelines
• Building and maintenance of data warehouses for data storage
• Ensure ETL data pipelines are secure and compliant based on company policies

Data Scientists are responsible for collecting and processing structured and unstructured data prepared by Data Engineers, cleaning and preparing it in a format that is usable and understandable by applying advanced programming languages and tools to build and develop predictive or prescriptive models.

Their typical job responsibilities include -

• Understanding business requirements and formulating them into data problems
• Collect structured or unstructured data using SQL, Web Scraping, etc.
• Data cleaning by discarding irrelevant information and handling NULL values
• Extensive data exploration using programming languages such as Python, R, etc.
• Developing predictive and prescriptive models using various machine learning or deep learning algorithms

Data Engineer vs Data Scientist - Salary

As it is evident both these job profiles attract high salaries. Data Scientists and Data Engineers are already in demand that is expected to be there for the next decade as well. Now is the time to upskill yourself if you wish to make a career in any of the above profiles.

These estimated figures are based on the Glassdoor survey and data taken from AmbitionBox.

Data Engineer vs Data Scientist - Career Growth

Data Engineer is not an entry-level role. This role requires prior experience in handling systems and infrastructure that are important for the implementation of big data technologies.

Many professionals start their careers in the software engineering field and leverage various roles such as Database Developer, etc. to sharpen their data engineering, data processing, and cloud computing skills before transitioning into Data Engineering roles. As they gain more experience, they can move into managerial roles or become a Data Architect, Solution Architects, or ML Engineer.

Many Data Scientists are hired in entry-level data science roles such as Junior Data Scientists that give them opportunities to develop and sharpen their technical skills before moving to senior roles such as Senior Data Scientist etc. or managerial roles such as Data Science Manager, etc.

Both job profiles offer great career growth from technical as well as managerial points of view.

Data Engineer vs Data Scientist

We have discussed how these two job profiles differ from each other based on many aspects in previous sections.

Here we summarize these differences and put them in a tabular format -

Data Scientist vs. Data Engineer: Which is Better?

The next question that might come to your mind is which job profile is better for a career? There is no definite answer to this question as they both are high-demanding roles and it entirely depends upon your interests and educational background.

Consider Being a Data Engineer

• Data Engineers are mostly undergraduates from software engineering fields such as Computer Science or Information Technology, etc. as they need to have strong coding skills to build the data infrastructure. They also need to stay tuned with the ways they can continuously improve existing systems, and infrastructures to save organization's money and resources.

So if you have a degree in Computer Science or related fields and have an interest in systems, programming languages, databases, and related technologies, Data Engineering might be the right path for you.

Consider Being a Data Scientist

• It is very common for Data Scientists to possess bachelors or advanced degrees in engineering, statistics, or related fields. They have a passion to play around with numbers and data to detect various patterns or trends in it by applying advanced data science techniques using programming languages.

If you are an analytical thinker who likes to perform analysis on the data by implementing machine learning algorithms by writing codes using programming languages then The Data Scientist path might be suitable for you.