Data Engineer Vs Data Analyst Vs Data Scientist


When entering the world of data, there can be a lot of words dumped on you that seemingly mean the same thing. The worst offenders of these are the jobs themselves – is there really a difference between a Data Scientist, Data Analyst, and a Data Engineer?

As a matter of fact, there is, and you’ll find out all about it today.

With such a wide variety of options to choose from, you may be asking quite a few fair questions: which direction should I take my career? How do their responsibilities differ? Do they require the same skillsets? Today, you’ll learn about the similarities and differences between these three enticing careers, as well as the various skillsets you may need if you want to pursue one of them.


Let’s start with some basic definitions. A data analyst is pretty much exactly what it sounds like – these guys (or gals) use mathematical and statistical techniques to find patterns in numerical data that can help companies make more informed decisions. They don’t focus as much on the more complex Big Data, nor do they focus on preparing or cleaning data; they’re more numerical and focused on practical data that can help companies directly.

On the other hand, data engineers are far more technical. They use complex algorithms and programming languages that can help prepare and display data in a format that is useful to clients and stakeholders. In a way, they encompass some aspects of what Data Analysts do, but the Data Engineer’s job is far more boots-on-the-ground and knee deep in databases.

A Data Scientist takes aspects from both of these jobs – effectively performing the Data Analyst’s job at a much bigger scope. They examine large data sets that aim to provide insights to the future – which means they are generally far more complex and require the manipulation and cleaning that a Data Engineer would typically perform.


The obvious commonality between these three jobs are, well, data! While the goals and day-to-day operations may differ, all three of these roles utilize mathematical models and statistical analysis in order to manipulate data and reshape it into something that can be useful.

As a result, it’s possible that all three of these roles will involve at least a little bit of SQL for organizing data, as well as some Python or R for finding trends and visualization. Applying some knowledge of statistics, mathematics, as well as models from machine learning will be something that all of these guys will have to do, although probably to varying extents depending on the company and what it is they want out of their data. It’s also highly likely that you’ll be working as a team in all of these roles, so you better have some readable code.


So, it’s pretty clear that these roles share similar skills and responsibilities, so how are they actually different? Does it actually matter which one you choose? Well, let’s analyze this role by role, starting with the Data Engineer.


In the data life cycle, it basically always starts with the Data Engineer. These guys don’t focus on analyzing the data as much – rather manipulating and building it in a way that means it can be easily analyzed, whether by a business or by a professional data analyst or scientist.

This isn’t just writing some fancy SQL queries, however – Data engineers are also responsible for the production of platforms and architectures specifically for data processing, so it’s possible that they would typically possess a full range of skills that allow for the development of some high-quality software.

Arguably, a Data Engineer is closer to a software engineer than a Data Scientist – although all Data Scientists rely on the work of Data Engineers in order to make accurate predictions and analyses.


Out of all the roles mentioned today, the Data Analyst is probably the one you’re going to start with – at least according to the average salaries. Data Analysts come in at a far lower salary than Data Engineers and Data Scientists, which should give you the idea that it’s one of the easier roles in the data life cycle.

While a Data Engineer will be more versed in back-end development and relational databases, a Data Analyst will specialize in visualization, cleaning, and short-term predictions based on numerical data.

It’s possible that you may not even need to do any programming as a Data Analyst – rather relying on the handy tools developed by data engineers. It’s less math and computer-science heavy, and more focused on the business side of working with data.


This is the big one – the one you’ve probably heard the most about, and for good reason. The Data Scientist incorporates aspects from both the Data Engineer and the Data Analyst in a hybrid role that aims to make predictions from massive, complex, categorical data sets using advanced statistical models such as neural networks.

In essence, a Data Scientist uses the statistical and mathematical skills that are used by a Data Analyst, and combines them with the computer science and programming skills that a Data Engineer uses to create tools that allow for predictions to be made that are backed up by a lot of data.

Companies will typically choose a Data Scientist over a Data Analyst if they have a large amount of complex data that may not reveal any insights on the surface, but can provide some useful information with some machine-learning magic.


While there may be quite a bit of overlap between the roles and skills of these jobs, the road you need to take for training for each one varies, so it’s important to understand which one you wish to pursue and the kinds of skills you need to have in order to succeed.

A Data Analyst will focus more heavily on their communications skills – not just verbally, but through visualizations, graphs, and charts. As a result, a Data Analyst would focus on becoming proficient with data management tools such as excel, Oracle, SQL, and data visualization tools such as Tableau, Domo, and QlickView which will allow them to both analyze data effectively and present it in a form that can be useful to the average businessman in a board-room meeting.

On the flip side, a Data Engineer will work almost completely in the back-end, so their skills will be far more focused on programming and software development, particularly in languages such as Python, Java, Scala and other technologies such as Apache Spark, Kafka, and Hadoop.

A Data Scientist will specialize in math and statistics, although they won’t skimp on the programming. The primary focus of a Data Scientist will be on machine learning models (such as supervised learning, regression, decision trees, and classification) which can help them fully unpack the hidden insights in any given data set, regardless of how big or complex it may be.

But, in order to implement these algorithms and models effectively, they need to have a deep understanding of programming languages such as Python and R, which are useful for data manipulation and modelling.


Now that you understand how these career paths differ in skillset and job requirements, let’s take a look at how they differ in pay. We’ve crunched the numbers on employee salaries in three major tech cities; San Francisco, New York City and Austin, Texas.   (All of our salary averages are base salary only, and don’t include equity from startups or FAANG RSUs. )

In San Fran, Senior Data Analysts can expect an average salary of $133,000 per year, with Senior Data Scientists hitting the $183,000 mark. Data Engineer salaries fall somewhere in the middle at $143,000.

In the Big Apple, Analysts, Engineers and Scientists are offered $106,000, $123,000, and $158,000 respectively.

Lastly on the list, if we head to Austin, Texas, Senior Data Analyst roles are being advertised at an annual rate of $98,000. Data Engineers take home $104,000 per year.  If you’re a senior data scientist, however, expect a six-figure salary averaging at around $127,000 per year.

We hope that you now clearly understand the differences between these 3 amazing and in-demand career paths. For further career assistance contact Kofi Group today! If you enjoyed this blog you may also enjoy our other blogs like, Do You Need To Know Data Structures and Algorithms or Data Science: How to Get In and What Hiring Managers Want.