Profile Difference between- Data Engineer, Data Analyst & Data Scientist

Data is emerging to be one of the most influential assets for corporations. Decision-making, business expansion, and executions depend on data.

Data is emerging to be one of the most influential assets for corporations. Decision-making, business expansion, and executions depend on data. However, operating data requires a systematic approach. It is divided into several layers. 

But for execution, you need to gather data from reputable sources. Furthermore, data can be erroneous. For that, data is cleaned. Once the information is ready to be processed, data visualization is required for presentation. These are some of the responsibilities divided between data engineers, data analysts, and data scientists. Their profiles vary, with differences in skill set. You will find online that a data analyst works on different problems, and requires different tools than a data scientist and a data engineer. However, the only similarity is that their responsibilities revolve around data. There are platforms to learn technology and programming languages online which helps you to gain in-depth knowledge of the domain.

Let’s look at the differences between these profiles to get a better idea of their roles. 

The role of a Data Engineer

Data can be considered a cocoon that goes through several layers of transformation before turning into something beautiful and valuable. 

A data engineer lays a foundation for data scientists and data analysts. Data scientists are equipped with technical skills which allow them to extract data from unstructured and structured sources. Furthermore, they gather data at a single, accessible destination for all the key businesspersons. 

Data engineers carve out paths for collecting user data and build database systems for storing them. Moreover, they also store the data on cloud systems. For creating efficient databases, data engineers also carry the experience of cloud computing. They are skilled with programming that helps them set data warehouses. 

Data engineer responsibilities include building data pipelines that can be used to process data in the future. They prepare the outline of database systems and models that are used for data processing at every step. 

Data engineers carry the necessary skills of SQL, which helps them with the smooth retrieval of information. The knowledge of SQL gives them an edge in identifying all the essential chunks of information. Moreover, data engineers also carry software development knowledge, which helps them with the architecture of database designing. This will give you a broad idea of data engineer skills. 

Now that data is ready we need someone to analyze that data.

Responsibilities of a Data Analyst

We are at that stage where a cocoon transforms into a caterpillar. 

Data is ready to be analyzed. A data analyst dives into the depth of data for preparing insights that solve existing business problems. Data analysts are responsible for asking questions that others aren’t focusing on. They identify issues by consuming data and presenting their findings to businesspeople. 

Data analysts understand the business principles through data. They carry statistical and mathematical knowledge for interpreting data. The responsibilities of a data analyst also include the use of programming and database languages for performing computing tasks. They carry the knowledge of SQL, which helps them interact with data present in relational database systems. 

Analysts are also skilled with Microsoft Excel skills. Excel helps data analysts store raw datasets structurally and perform computing functions efficiently. They prepare data in an easy-to-access manner. The non-specialist data guy in your organization can read data easily. And preparing data in such a manner is the responsibility of a data analyst. 

However, data analysts are also required to present their findings to business managers and decision-makers. They do so by involving data visualization, communication, and critical thinking. After all, presenting insights is essential to formulate decisions that help businesses thrive.

We have the data with us. We have also identified the problem. But now, we need to work towards the solutions. 

Who can help us? 

Role of a Data Scientist

Our caterpillar has transformed into a pupa. It’s just about ready to fly. However, there’s some time. 

Data scientists also consume data. The role of a data scientist is to consume data to identify patterns and trends in data. Data is useful for staying updated with the trend. A data scientist uses technology and mathematical as well as statistical knowledge, to churn data. Along with data reading, data scientists are also assigned the duty of building predictive models. 

Predictive models are useful for identifying future problems that a business might face. It’s through identifying discrepancies within the data generated by the organization. Moreover, data scientists prepare predictive models with the help of machine learning and deep learning principles. 

Machine learning and deep learning are powerful weapons of a skilled data scientist. These powerful weapons help a data scientist in statistical modeling and work on algorithms that provide business solutions. Data scientists also use advanced programming skills for playing with complex datasets. A data scientist works on complex mathematical computations and breaks down his findings in an easy-to-understand manner. It helps key stakeholders and all the parties to be on the same page. 

But apart from that, data scientists also clean data, organize it, and keep it structured. They also take care of outliers in data. And, strong interpersonal skills help them communicate their decisions to all the members of the organization. 

We have summed up the responsibilities of all three roles briefly. 

We can conclude that for making important data decisions, all three profiles are vital. One of the three gathers and extracts raw data from various sources. Once data is extracted it’s ready to be processed. Data processing reveals the areas that require attention. Once the problem is identified, as an organization, you need to work on the solutions. That’s when building data models come to the scene. Apart from it, other important practices such as predictive analysis and error analysis also help in solving business problems. 

To conclude, even though the three profiles sound similar, there are stark differences between them. A data engineer focuses on gathering data and making it accessible to other parties. A data analyst delves into the depth of business problems and churns data to find answers. The role of a data scientist is to work towards the resolution of the identified business problems. 

It’s okay to say that data engineers gather raw materials for the recipe. Data analysts decide what to prepare. And data scientists are expert executors. All three of them prepare the dish. If anyone’s missing, then the final dish might not be tasty. 

Also, we have reached the final stage. The pupa is now finally a budding butterfly, ready to fly. 

[zombify_post]

Leave a Reply