Data Engineer and Data Scientist: What are they? How are they different?
What are a data engineer and a data scientist, and how are they different? This question might occur in your mind because a data engineer and a data scientist are the terms that we often hear lately since they are one of the fastest-growing jobs.
We might haven’t understood the difference between a data engineer and a data scientist clearly. Today, Sertis would like to introduce you to these high-demand jobs - what they are about, which skills are required, what kinds of personalities suit each job, and what their tasks and responsibilities are.
If you are planning to kick-start your career in the world of data and are finding a job that is right for you, let this article help you make the decision.
What is a data engineer?
A data engineer is responsible for developing, testing, and maintaining the architecture that stores and manages raw data. The focus of the job is to build and maintain the architecture that facilitates extracting and transferring data, making data ready to use at any time.
A data engineer develops data infrastructure, such as a database, a data lake, or a data warehouse, as well as builds a data pipeline that automatically transfers data from one place to another destination. A data engineer is also responsible for other data set processes, such as data modeling and ELT. To conclude, a data engineer is someone who takes care of the data infrastructure that enables further uses of data.
Being a data engineer requires the knowledge of a variety of tools to manage data with different formats and from a variety of sources, and the knowledge of infrastructures such as cloud infrastructures, e.g., AWS, Azure, and GCP, to effectively build and implement a data architecture on each specific infrastructure.
Apart from developing and implementing, a data engineer is in charge of maintaining and ensuring that everything works smoothly in place, while looking for a room to make system improvements.
A data scientist makes use of data that is prepared by a data engineer to analyze and come up with the solution of what clients are looking for. Some may wonder what is the difference between another demanding job, a data analyst.
(Read more to find out about a data analyst in A Day in the Life of a Data Analyst: What Do We Do Daily?).
Actually, there is a unique difference between these two jobs.
Data analysts focus on going through specific data to find the specific business answer for clients. It requires deeper business acumen, while a data scientist builds a tool to automate the analytics process for clients by utilizing machine learning models and statistical analytics with a large volume of data. A data scientist conducts predictive analytics such as predicting sales and prescriptive analytics such as optimizing operations by using AI and machine learning to reduce cost and optimize production scheduling.
The core deliverable of a data scientist is a tool or application that automates data analytics for clients.
To conclude, the difference between these two jobs is that a data engineer is someone who lays down the data infrastructure, while a data scientist jumps in to develop the systems and tools to analyze the data.
Are you a coder who likes to build things from scratch, lay down the infrastructure, and is eager to learn and find new tactics to improve the infrastructure? And are you the thinker who likes to cultivate knowledge and contemplate what you can build to enable others to work faster and easier? If the answer is yes, a data engineer is a right choice for you.
If you are interested in statistics, algorithms, and machine learning models, if you are a curious person who always comes up with questions and is enthusiastic to find the answer, by making assumptions and proving them with data, and if you find pleasure in analyzing and predicting the possibility, a data scientist is what you should go for.
Examples of what a data engineer and a data scientist do
Company A needs to make use of its scattered data but they don't have an effective and organized infrastructure and pipeline.
Data engineers will be responsible for this project. They will work with clients to understand the formats, sources, and structures of the company's data, in order to select the tools and platforms that suit the requirements. Then, they will build a data warehouse to store and organize data, and a data pipeline to transfer data to servers, making them accessible to other users. Data engineers will also be responsible for the maintenance of the system, making daily data processes, e.g., extracting, transforming, and loading, run smoothly and properly.
Company A now has a ready-to-use data infrastructure and needs a system to predict monthly sales.
Data scientists will be responsible for the project. They will start by analyzing the client's data to look for trends and patterns in sales records and transforming the results into a feature for machine learning model training and development. They will train models on what predictions they should make when they find a specific characteristic of a phenomenon. Then, they will deploy the model as an application that can automatically analyze and provide real-time insight to hand over to clients.
Sertis is hiring. Job opportunities for a data engineer, data scientist, and other positions in the data world are open for those who are ready to grow with us. Check out our open positions: https://www.careers.sertiscorp.com/jobs.