What is the difference between Data Analyst,Data Scientist and Data Engineer?

Makenow | 11 FEB 2022

The data area is constantly growing in the market. Every time companies build and expand data teams and thus, new opportunities arise. This is a field where there is strong multidisciplinarity with many skills, knowledge and technologies interacting with each other. These characteristics are reflected in the different professions in the data world. They are data engineering, data science, data analysis, among others. The program has a wide range of positions and we will tell you a little of the ones mentioned above. Before we start, it is important to raise a point: there is a lot of overlap between the positions in this area, so the big difference will be in the responsibilities and final objectives. But, after all, what does each of these professions do? How do they interact and act in a data project?


A professional in this area is responsible for creating data pipelines, maintaining and building data storage systems that will be used by other areas of the company. Data Warehouses and Data Lakes are examples of such structures that can be consumed by analysts and data scientists. In data engineering, you work with cloud computing services, big data tools, relational/non-relational databases and among other technologies. This role is essential to building the foundation of a data project.


In this case, a data scientist is responsible for connecting science and analysis with the business world. He/she creates solutions, answers questions and assists in decision making based on studies or experiments based on data. Thus, it brings insights or even prediction models, recommendation, among others. In the field of data science, there is a lot of programming, data cleansing, statistics, and it can employ more advanced techniques such as machine learning and deep learning. This collaborator is very important to optimize processes, bring impact to the business, solving complex problems at scale.


In turn, a machine learning engineer, or machine learning engineer, is responsible for transforming the insights and models produced by a data scientist into a software product. Whether in the form of an API, microservice, system, or even schedule code. This professional uses software engineering and machine learning concepts to ensure that the application always works and is monitorable. That way, you can quickly resolve a problem as soon as it appears. It is worked through programming, cloud computing services, versioning, statistics, machine learning and systems design. The machine learning engineer is a key part of ensuring that the advances made by a data scientist are put into production and effectively bring value to the business.


The data analyst is responsible for bringing insights and solving business problems by analyzing well-defined databases. This is done through graphs, reports, metrics, among others. This is the most “traditional” profession in the data medium. Here you work with statistics, visualization, database queries, among others. This is a very important vacancy to optimize, describe processes, find underutilization of resources or bottlenecks, bring insights beyond other functions.


Ready! We now have a better understanding of these emerging professions. It is worth mentioning that it is very common to play more than one of these roles within a project, depending on the maturity of the company's data team. Is that you? Do you think about pursuing a career in this area that grows every day or acquiring knowledge to use it as a tool in your day-to-day? For that, check out our Data Science and Artificial Intelligence course and the other Let's Code courses!