To understand the difference between a data engineer, a data scientist, and a data analyst. We must first understand what functions and processes data-related jobs cover. This is a massive topic in itself. I won’t be covering each function and step involved in the data science process. Instead, I will explain how the data engineer, the data analyst, and the data scientist contribute to this process.
The data engineer is usually responsible for the initial stages, where they have to develop an architecture for database storage and pipelines. This Mainly includes ETL(extracting, transforming, and loading the data.)
The Data engineers extract massive amounts of data as required by the final consumers of the data. They also transform, arrange, store and automate the flow of data, so it is readily available.
The analyst is often responsible for the translation of data and the generation of meaningful output.
While it is true that the data engineer does the majority of transformation and arrangement. The data which typically flows into a data science pipeline after ETL will in most cases not be in the form of solutions or insights.
A data analyst’s responsibilities include, carefully understanding -
- How the data can be used?
- What insights the data can provide?
- To what extent do these findings affect an outcome?
- What KPIs can be set to measure success, etc.
An analyst mainly prepares dashboards, Reports and spends a lot of time manipulating data to extract information from it.
A data scientist in a raw sense is someone skilled in data science as a whole. At least this is my understanding. Data science is the culmination of all the steps involved in turning raw data into valuable insights and predictions.
But generally speaking, data scientists are in most cases in charge of more advanced analysis, Research, and Development. And to be honest, different organizations have their descriptions for this role.
However, from what I’ve experienced, A data scientist typically tries to understand how data and insights can be used to potentially solve a problem or predict a future problem. Most data science roles require skills in machine learning. While this is not the case for data analytics roles.
Have you made your choice?
All three roles are equally important in the data science process, and where you want to specialize should always be based on your preference and skillset.
So, have you decided what suits you more?
Or, if you’re already working in any of the three roles, does your job description match my understanding. If you have another definition/understanding for any of these roles, please do share them with me, so we can get a more dynamic understanding of each of the roles.
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