Data Scientist vs Data Analyst vs Data Engineer

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A breakdown of the most popular data related careers in 2022

 
Over three years ago, I was faced with a decision that would stick with me for the rest of my life — “what am I going to do for a living?” I had just completed my higher education and was fresh out of high school.

After a long discussion with my friends and family, I settled on the “sexiest job of the 21st century.” I decided to pursue an undergraduate degree in data science.

At that time, I picked data science because I was unaware of my options. I heard about a popular field that promised flexible working hours and a thick pay-check, and decided to specialize in it.

After over a year of working in the data industry, however, I have come to realize that data science is just one of the many career paths I could have chosen. 

There are many less popular roles in the data industry that are high in demand and pay well.

In this article, I will describe three of the most promising career options within the data industry  — data analytics, data science, and data engineering.

Data Engineers

 
Data engineers are the unsung heroes of the data industry. They consolidate large amounts of data and build scalable pipelines that can be easily accessed by other data professionals.

Data scientists wouldn’t be able to build machine learning models without all the data preparation done by data engineers.

The demand for data engineers has grown in the past few years, as companies have started to realize the importance of having a scalable data framework in place.

Data engineers are the most technical out of the three roles in this list. They design database schemas, manage the flow of data within the system, and perform quality checks to ensure that the data is consistent.

In order to become a data engineer, you need to possess skills in software design, database architecture, devops, and data modelling. You also need to have a strong command of SQL. Knowledge of scripting languages like Python and Bash is usually a requirement in data engineering job descriptions.

Data Analysts

 
Data analysts are individuals who organize data to identify trends that can support in decision-making.

These individuals use their technical and domain knowledge to come up with recommendations that can help businesses grow.

Here is an simple example of a data analyst’s workflow:

• Store ABC would like to understand their customer base better.
• They want to segment their customers into different groups based on factors like brand loyalty and amount spent during each purchase. They will then entice each of their customer segments with different promotions.
• A data analyst can identify trends based on customer purchase behaviour and perform this segmentation.
• For example, there are a group of customers who used to frequent store ABC every month (Group I). However, in the last few months, they suddenly stopped making purchases. This means that they might have decided to shop at a competitor brand, or they simply don’t require the product anymore.
• A second group of customers only frequent store ABC when a specific product is on sale (Group II). They aren’t regular customers, and only respond to promotions featuring a certain item.
• These two groups of customers need to be approached differently. Group I customers exhibited brand loyalty, which needs to be regained by tactics like personalized messages and gift cards.
• On the other hand, group II customers should be targeted with specific promotions based on products they frequently purchase.

Data analysts generally perform tasks like the one described above.

To identify customer value and group them like above, analysts need to have a strong understanding of the company’s product offering. They also need to have domain expertise in fields like business and marketing.

Data Scientists

 
The job scope of a data scientist is often confused with that of a data analyst, and this is because there is a huge overlap in their skillset.

However, the main difference between these roles is that data scientists build machine learning models, while data analysts don’t.

A data scientist needs to possess skills that are very similar to that of an analyst. They need to understand how to collect and transform data, create
visualizations, perform analytical tasks, and solve business problems with the help of data.

Along with all the skills listed above, data scientists also need to know how to create predictive models.

Here is an example of a data scientist’s workflow:

• Store ABC wants to understand the lifetime value of their customers. A data scientist will perform all the analysis explained above.
• Then, they will go a step further to build a clustering model to segment these customers into different groups.
• To come up with personalized product recommendations based on each customer’s preference, a data scientist can also build a recommender system within each segment.

Conclusion

 
Data science is extremely popular, and there is a lot of hype surrounding the field. However, there are other careers in the data industry that are growing rapidly, and are equally promising in terms of salary and demand.

Data scientists, engineers, and analysts are equally important to the data lifecycle. Organizations require expertise in all these areas in order to come up with data-driven decisions that add business value.

 
 
Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.