Data Engineers, Data Scientists & Data Analysts | What’s The Difference?

Back in 2012, The Harvard Business review dubbed Data Science the sexiest job of the 21st century. And for the most part, that still rings true today. Even with the rise of AI-powered data science platforms, the demand for Data Scientists, Data Analysts, and Data Engineers continues to grow as more and more companies look to harness the power of big data. They need people (or machine learning) to turn this data into valuable insights. Yet, even with the popularity of the data science industry, the various job roles and functions are often confused.

In this article, we’ll take a look at the difference between three of the most popular jobs in data science: Data Engineer, Data Analyst, and Data Scientist. We’ll explore their functions, and the skills or education you’ll need to land one of these jobs, if that’s your goal.

Data Engineer

General Job Overview of a Data Engineer

Data engineers create and optimize data-processing systems. They are more technical and essentially enable data scientists and analysts to do their jobs. Companies depend on having accurate data that can be accessed and used by individuals and teams, therefore data engineers are needed to ensure that all data created and received is made available, properly handled, and stored. These are typically called ‘data pipelines’.

Since a data scientist or analyst must be able to concentrate on the analysis of problems, rather than transferring data from one source to another, data engineers are hired to focus on the creation and maintenance of these data pipelines. They usually specialize in using sophisticated tools and instruments like Python, Spark, AWS, GCP, Airflow, and many more to handle data on a large scale for a company.

Typically, data engineers have a strong background in computer science and software development, which helps them to build pipelines and orchestrate them. They are also skilled at integrating external datasets.

Educational Requirement of a Data Engineer

It may come as a surprise considering Data Engineer is the highest paying job role of the three mentioned, but it actually has the least ‘formal’ education required. For most job’s a bachelor’s degree is all that’s required. However, for some companies, formal education may not be a requirement if you have the necessary skills, job experience, or certifications.

Data Scientist

General Job Overview of a Data Scientist

In the data analytical world, data scientists are what some would call a “jack of all trades”. Their role aims to bring together all aspects of a project while working with those who have more specialized skills, like a data engineer. A data scientist usually spends maximum time in data transformation. A data scientist’s role can include translating the company’s goals to other team members, collecting data, creating machine learning models, researching and developing various approaches, creating data visualizations and presentations, and more.

They can identify patterns and make better predictions using Machine Learning models. Scientists are more likely to ask questions and solve problems based on models than analysts who often just describe the trends. According to the US Bureau of Labor Statistics, data scientists could see a 22% rise in demand by 2030.

Education Required for a Data Scientist

Most data scientist jobs require an advanced degree (Master’s or Ph.D.) in the same specifications as an undergraduate degree. For example, a Masters in Data Science, Computer Science, or Information Technology. Besides, having specialization in medical, air, space of biological sphere or education is highly appreciated and considered for becoming a Data Scientist. However, educational qualifications alone cannot help you fetch a job as a Data Scientist. Having advanced knowledge in this field is a major deciding factor.

Data Analyst

General Job Overview of a Data Analyst

The essential role of a data analyst is to help a business visualize data and make better decisions using data to answer specific questions. They are also usually in charge of communicating that data through reports that show insights and trends. A data analyst would be responsible for performing A/B tests and keeping track of web analytics.

For example, questions such as “When should sales reps target specific demographic groups?”, “What is the best way for a CEO to make sense of recent company growth?” data analysts are able to answer based on the results of the analysis. A data analyst can also help foster a greater connection between teams by explaining executive dashboards. Therefore data analysts are a great benefit to both technical and non-technical departments in a company.

The Takeaway

All three job roles have their unique functions and require different skill sets and education levels. Data Scientists and Data Analysts have the most similarities when it comes to their job functions, however, Data Scientists generally earn more than Data Analysts and require a higher level of advanced education.

Data Scientists tend to focus on the overall project while Data Analysts are expected to closely analyze and answer specific questions to a project. On the other hand, a Data Engineer’s job is significantly different than the others, as it requires high technical skills and knowledge of building SQL and NoSQL databases, controlling data flow, and preparing tables. It is also the job that pays the highest out of the three.

Since all three roles hold great growth potential, there are many career opportunities to take advantage if you have the interests and right educational background and/or skillset. However, if you are looking to start or grow your career in Data Science but don’t have the skills, DEVrepublik offers courses to help you do so.

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