Data Scientist or Data Engineer: Educational and Career Opportunities

Data science is defined as the discipline of extracting actionable insights and information from raw data. The field offers many job opportunities, which poses a challenge for people looking for the best fitting data science career. Sometimes a little direction helps.

On February 11, 2021, this career webinar with industry expert Ronald van Loon offered answers for anyone wanting to choose between becoming a data scientist or a data engineer. Here’s what Ronald had to say about these two popular data science careers and what you should consider when choosing between them.

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Key Differences Between the Roles of a Data Scientist and Data Engineer

Although they have some common characteristics, there are some notable distinctions.

Data Scientists Deal With:

By Contrast, Data Engineers Cover:

  • Programming languages
  • Database management
  • Data pipelines
  • Software-oriented issues
  • Helping to improve an organization’s efficiency
  • Improving data accessibility

Each career has a different business role, although there’s some common ground between the two positions. There might be occasional shifts in responsibilities, depending on the business, the industry, and unique situations that arise.

Data scientists solve business problems and come up with solutions using analytics. On the other hand, data engineers help data scientists get the information they need for their analytics. Both positions work together, using algorithms to foster business success.

Think of the two positions as two different positions on a football team. Each has its capabilities and limitations, but they’re both working towards the same goal, winning the game.

The Specific Skill Sets These Careers Require

If you need any evidence that these two careers are quite different, you only need to look as far as their required skill sets. Each job comes with its unique demands, and thus you need the right tools and skills to fulfill your role.

Your Typical Data Scientist Skill Set Consists Of:

  • Mathematics (e.g., statistics)
  • Machine learning, artificial intelligence, and deep learning
  • Basic programming (e.g., R, Python, Java), and other technological proficiencies like PyTorch, TensorFlow, and Tableau
  • Data platforms (e.g., Mongo, Oracle)
  • Analytics-related skills (e.g., data visualization, risk analysis, data mining)
  • Decision-making (soft skill)
  • A specialization geared towards an industry (e.g., healthcare, finance)

Data Engineers Need Skills Like:

  • Building, managing, and maintaining data pipelines
  • Putting data into models
  • Data warehousing
  • Data architecture
  • Programming languages and technologies (e.g., Python, SQL, Hadoop)
  • Communication and collaboration skills (soft skill)

Choosing the Data Science Career Best for You

Data scientists are best suited for good team leaders, possess excellent communication skills, are adept at building machine learning models, and are analytical professionals. Data engineers are suitable for people who are programmers or experts in software and data.

So, if you’re more of a tech-savvy, skilled programmer type who sees themselves using data to help the company in a behind-the-scenes capacity, then you want to be a data engineer. If you see yourself advancing into a managerial position while still having a set of technical skills, then choose data science for your career path.

The Impact of Emerging Industry Trends

The whole spectrum of technology is changing, and it’s changing rapidly. If you understand the trends better, you can leverage your data scientist and data engineering skills more effectively, no matter what business you work for.

The trends also help you understand the new technologies better and focus your upskilling efforts. Keep a close eye on these trends:


The COVID-19 pandemic has emphasized technology like robotic process automation (RPA) as more employees shift to at-home work. Automation also covers machine learning applications and software robotics. This technology also helps employees handle routine, repetitive tasks found in CRM and human resource systems.

Augmented Analytics

This trend deals with the rapidly growing cloud computing fields and the Internet of Things (IoT). The accelerating growth in data being produced and collected requires new analytics tools to turn it into actionable insights.

Natural Language Processing (NLP)

This trend encompasses conversational analytics and deep learning. If you have a Siri or Alexa, you’re familiar with NLP, which relies on conversational AI and voice recognition. NLP also covers sentiment analysis, named entity recognition, and coreference. These processes rely on extracting data from speech patterns. Today’s technology boasts over 95 percent accuracy in speech recognition, which is the human recognition level.

AI and Intelligent Applications.

Data scientists and data engineers are crucial for these growing trends, including supply chain management, logistics, agriculture, and security. Security is especially paramount, given the prevalence of cybercrime and the increased reliance on keeping data secure thanks to the increased demand for working from home — another side-effect of the pandemic and social distancing.

Each Field’s Educational Path

These two data science careers have sets of rigorous demands for anyone who wants to take either path. The data scientist field has among the most challenging requirements in the IT industry. Bear in mind, most data scientist positions require at least a Master’s or Ph.D. degree.

The aspiring data scientist needs to be literate in statistics and mathematics and knowledge of programming and sciences. After this, the candidate should learn algorithms, information visualization, data structures, and other CS disciplines. Finally, the data scientist could benefit from acquiring some related certifications to fill in skill and knowledge areas.

It’s a little easier for data engineers. They require an undergraduate degree in mathematics, business, or a science-related major. After getting that degree, the would-be data engineer should focus on a higher education degree in a major like engineering, applied mathematics, physics, or computer science. And as is the case with data scientists, data engineers should consider certification in related data engineering disciplines.

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The Careers Available to Data Scientists and Data Engineers

Data is the engine that drives our work and personal lives, so there are many different, exciting careers available for data scientists and data engineers.

Data Scientist Careers

  • Data analyst
  • Machine learning engineer
  • Application architect
  • Business analyst
  • Statistician
  • Database administrator
  • Business fields such as consultant, sales, product development, and business development

Data Engineer Careers

  • Hadoop developer
  • BI developer
  • Technical architect
  • ETL developer
  • Data warehouse engineer
  • Quantitative data engineer
  • Data platform engineer
  • Data infrastructure engineer
  • Data warehouse engineer
  • DevOps engineer

Some of the top industries and domains that have a greater demand for data scientists and engineers include:

  • eCommerce
  • Tech Sector
  • Financial Services
  • Healthcare
  • Consulting
Looking forward to becoming a Data Scientist? Check out the Data Scientist Masters Program and get certified today.

Choose Your Path

No matter which data science path you choose, Simplilearn has a program perfectly suited for your career-enhancement needs. Aspiring data scientists should check out the Post Graduate Program in Data Science, in partnership with Purdue University, and would-be data engineers should look at the Post Graduate Program in Data Engineering, also in partnership with Purdue University.

Simplilearn also offers a wide range of related certifications that help data science professionals improve their skill sets. Check out Simplilearn today and give your data science career plans a boost!

About the Author


Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

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