Data Scientists vs Data Engineers: Which one is better?
With the recent boom in big data analytics and using raw data to solve business roadblocks, data science job roles have spread out and transmuted at a rapid pace.
Data science and data engineering, while belonging to the same category, are job roles that aid each other for maximum performance.
Even though data science is still at the forefront, data engineering is catching up at a steady pace with its special system architecture skills. They serve as the intermediary between data science and data analytics by preparing data for analysis.
While both seem similar, there are a few differences with job outlook, qualifications, skills, and salary. We will show you the subtle difference between data scientists and data engineers to help you navigate your options better!
Here is a summary of this definitive guide to familiarize you with the topics:
- Data scientists process raw data into meaningful patterns through their knowledge of data mining techniques and analytical skills
- Data engineers design infrastructures that aid data scientists in their task by building data pipelines and sensible systems
- Data scientists earn more than data engineers as per data from employment websites because of the increased demand for data scientists
- While data scientists are familiar with tools like MatLab, SAS, Hadoop, etc., we acquaint data engineers with SQL, ETL, Jenkins, AWS, etc.
- Both data scientists and data engineers have abundant job opportunities because of the spike in data management issues
The two are still connected, but their synergistic nature makes one less powerful without the other. Here are some questions to probe your inquisitions further:
- What do data scientists and data engineers do?
- Can data scientists work as data engineers?
- Are data engineers paid more than data scientists?
- Which job is better data scientist or data engineer?
- What are the skills required by data scientists and data engineers?
Meanwhile, you can check:
Difference Between Data Scientist and Data Engineer
While data scientists perform statistical analysis and extract meaningful patterns from large datasets, data engineers work for the practical applicability of data acquisition.
Data scientists use advanced techniques like clustering, neural networks, and decision trees to derive meaningful conclusions. It is an extensive field that combines mathematics, statistics, computer science, and information science. Along with that, data scientists have knowledge in the business domain to facilitate the interpretation of their findings.
The crucial areas of data science include Big Data, Machine Learning, and Data Mining. These skills are specific and exhaustive for the functions that they cater to.
Data engineers are usually in charge of pairing and preparation of data for operational or analytical motives. Their role revolves around data architecture and experience in constructing, developing, and maintaining these structures.
Data engineers process data process stacks to accumulate, store, process and prepare the data for in-depth analysis. Along with that, they design data pipelines that collect, prepare, and transform all data into usable structures for data scientists.
Also read: What is Data Science
What Does a Data Scientist Do in a Company?
They work on cleaning data and develop patterns with the data they order to reach meaningful conclusions that contribute to the growth of a company.
With their analytical skills and broad knowledge of various data mining techniques, they work to find solutions for problems with the data they collect.
Here is a job description for a data scientist from Glassdoor:
- Work with stakeholders to identify opportunities for leveraging company data to drive scalable business solutions
- Mine and analyze data from company databases to improve product development, marketing techniques, and business strategies
- Assess the effectiveness and accuracy of new data sources and data gathering techniques while developing alternative solutions
- Develop custom data models and algorithms to apply to data sets
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes
- Develop company A/B testing framework and test model quality
- Coordinate with different functional teams to implement models and monitor outcomes
- Develop processes and tools to monitor and analyze model performance and data accuracy
What Does a Data Engineer Do in a Company?
Data engineers design infrastructure for data scientists to analyze and interpret. Through their logical mindset, they build data pipelines and arrange data into programmed and sensible systems.
The following is a data engineer job description from Glassdoor:
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies
- Build analytics tools that use the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics
- Work with stakeholders, including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data infrastructure needs
- Keep our data separated and secure across national boundaries through multiple data centers and AWS regions
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
- Work with data and analytics experts to strive for greater functionality in our data systems
Data Engineer VS Data Scientist Salary
While it varies from experience to skills possessed, these are the average salaries for data scientists and data engineers based on data from employment websites:
| Data Scientist | Data Engineer | |
|---|---|---|
| Payscale | $96,565 | $92,650 |
| Indeed | $114,675 | $119,582 |
| Glassdoor | $140,000 | $138,000 |
The difference in pay between data engineers and data scientists, while not outrageous, is still considerably big. It is stipulated that this difference between data scientists and data engineers is because of varying job demands for data scientists and data engineers.
Here is the pay spectrum for data scientists over their careers:
Meanwhile, here is the pay spectrum for data engineers:
To delve deeper into this topic, you can check this guide on Data Science Salary for a comprehensive breakdown!
Data Scientist vs Data Engineer: Tools and Skills
Data scientists perform statistical analysis and data visualization, while data engineers deal with company databases and their structuring.
A few must-have skills for data scientists are programming, machine learning algorithms, and big data. They are familiar with tools like MatLab, SAS, Python, R, Hadoop, etc., to analyze and interpret data.
On the other hand, a typical skill set for data engineers includes programming, data structures, and distributing systems. They are usually acquainted with tools like SQL, ETL tools, shell languages, Docker, Jenkins, Data Warehousing like Redshift, AWS for cloud computing, etc.
Out of all of your skills, there are a few that contribute to your suitability for a job. Recruiters are often on the hunt for those specific skills which makes it important for you to list them diligently on your resumes.
Given below are the skills important to a data scientist:
However, it’s another set of skills that recruiters check for data engineers:
Data Engineer vs Data Scientist Demand
LinkedIn’s 2021 Emerging Jobs report showed that data science and data engineer roles are growing by 35% annually. However, the 2020 report showed data scientists at #3 position and data engineers on #8.
As per data from Indeed, there are about 85,000 job openings for data engineers, whereas about 110,000 job openings are for data scientists. While companies prefer recruiting highly skilled data scientists and data engineers, the increase in data management issues has created a spike in demand for these positions.
Once you have gained skills and expertise from your job, you can move to higher positions. From entry-level positions, you can move up to senior and director positions with enough experience!
Here is what a typical data scientist career ladder looks like:
Data engineers have a unique set of opportunities where you can move from being a data engineer to data scientist. Here are career opportunities for data engineers:
Such an increase in demand means that it is an accepting job environment for you to send out your applications!
Also read: Data Science Jobs
Now, for the showstopper.
What is Best for You: Data Science or Data Engineering?
Data scientists and data engineers are similar yet vastly different from one another. If you are still confused about what you should choose, we have a little treat for you.
If your jam is working on cluttered data and using scientific methods and algorithms to derive insights, data science could be for you. Not only that, you would be able to work with businesses and help them grow their company.
That is not all.
You can work in healthcare, construction, retail, banking, and many more! The key factor being you have what it takes to identify problems by evaluating data and finding methods to solve them as well.
However, if you want to go a step further and design systems that facilitate data collection and storage, you would be better off as a data engineer.
Data engineering is not one of the fastest-growing jobs for no reason.
Jonathan Coveney, data engineer at Stripe says "There's a growing sense today that data isn't just a byproduct, but rather it's a core part of what a company does". No truer words.
Data engineers build the very foundation of systems that data scientists work on, as they have a deeper understanding of data architecture and manipulating that data. As a data engineer, you will be able to explore this field at the start of its boom.
Before we rank scores, let's establish that one is not better off than the other. Both are equally important in any organization, and it's their combined effort that brings the magic to the table.
How do you get started on either of these careers?
Data Engineer vs Data Scientist: Educational Background
A computer science degree can get you both places if you're still unsure. However, if you do not want that, you could do a degree in mathematics or statistics. Data science is a lot about extracting value out of data sets, and so a mathematical brain would be stellar on the role.
Data engineers also come from other backgrounds, but they usually come with a degree in computer science engineering.
Data Science vs Data Engineering Certifications
If you want to shift careers or are hesitant to commit to a degree, you can take certifications in data science and data engineering. As long as you build your skill, recruiters will be obliged to recruit you.
For more details, you can check out How to Become a Data Scientist for guidance on how you can prepare for this career!
Do You Need to Learn Coding?
It's a trick question.
Now, while it is not imperative to be a coding master, a fair bit of knowledge, especially in R and Python will come in handy for you!
A typical data science job description will entail a degree of acquaintance with coding skills to help you do your job better.
Sorry, folks.
You absolutely need to be a coding master when it comes to data engineering. A typical data engineer resume will show you an extensive list of technical skills, in which programming languages take a major chunk!
Also read: How to Become a Data Scientist
Which Field is Better: Data Engineering vs Data Science
There are pros and cons to both fields, as they are still in the developmental phases. The responsibilities are not cut differently yet, and there might be a lot more mixed duties than what was accounted for.
Can data scientists work as data engineers?
In the current scenario, yes!
Data scientists are trained to manage big data and derive solutions, which will cover the basic tasks of a data engineer as well.
As a data engineer, you will also be able to join as a data scientist because you will be familiar with the concepts that they deal with.
However, if you made a career switch and say you have a degree in an unrelated field, you would have to take additional courses to be on par with the knowledge of other data engineers.
Since data scientists do not have to be technologically sound, the number of tasks will be vast and limited at the same time. In this situation, data engineers are more desirable as they can perform optimally with the tasks they are given.
You can always test the fields before you make a commitment, but these two factors may give you a bit more insight:
- Study shows that there are five times more job openings for data engineers than for data scientists
- Data scientists try to find better job opportunities and are less likely to stick to their career
The only thing left to do here is taking a long and analytical look at your skills, interests, and general facts about both fields. With the right pros and cons list, you will be able to make your decision in no time!
Key Takeaways
Due to the nature of their work, data scientists and data engineers do different tasks to achieve the same goal. However, instead of choosing one over the other, companies keep a healthy mix of the two to reap maximum benefits out of the power duo.
Here is what you now know:
- Data engineers prepare data for analytics, while data scientists perform statistical analyses of raw data to extract useful patterns
- While the average salary of a data scientist is $117,080, data engineers earn a yearly average of $116,744 because of their difference in demand
- Data scientists show familiarity with tools like MatLab, Hadoop, and programming languages like Python and R, while data engineers are acquainted with tools like SQL, ETL, AWS, etc.
- Both data scientists and data engineers have a degree in computer science, but data scientists can work with a degree in statistics, mathematics, etc., as well
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