Data Analytics, Data Engineering, Data Science: The Differences and Intersection

5 min readMay 10, 2022

Many businesses and start-ups rely on data nowadays. In particular, those in the digital or online space. Today’s world is based entirely on data, and no firm could thrive without data-driven strategic planning and decision-making. Data plays a big part in today’s economy and the daily job of many employees, whether it’s tracking consumers’ behavior and purchases on an e-commerce site, comparing last year’s performance to this year’s, or evaluating the number of visitors a website receives. For business planning and development, it is a critical and useful asset. Because of the vital insights and trust that data provides, there are various professions in the sector today that deal with it. In this article, we will discuss the key differences and similarities between a data analyst, a data engineer, and a data scientist.

Before we get into the details, let’s go through everything that will be addressed in this article:

1. Differences and Intersection

2. Data Analyst, Data Engineer, and Data Scientist: An Overview

3. Skill Set

4. Roles and Responsibilities

Differences and Intersection

The obvious intersection between the roles of a data analyst, a data scientist, and a data engineer is that they all work with data. The distinctions between a data analyst and a data scientist, and how these two are related to data engineering, are frequently confused. However, there are some significant disparities between these three fields. Data analysts process and interpret information. A data scientist must be able to create and develop data processing technologies. A data engineer must also be able to create programs or systems that can transform data into useful information that can be analyzed. In the following section, we’ll delve deeper into this.

Data Analyst, Data Engineer, and Data Scientist: An Overview

Data Analyst:

The process of the extraction of information from a given pool of data is called data analytics. A data analyst is a person who engages in this form of analysis. A data analyst extracts the information through several methodologies like data cleaning, data conversion, and data modeling.

In essence, a data analyst examines data and breaks it down so that companies and teams may make decisions based on it. Knowing which landing page of a website works best in terms of SEO, or how many users leave a web page right after visiting, for example, can be quite useful in determining the next stages in a content strategy. A data analyst examines the data and delivers it in a way that teams can understand. They may need to assess current performance, plan for the future, and develop strategies to improve sales or website visits, as well as identify trends among various user groups. Data analysts are one of the data consumers. A data analyst answers questions about the present such as: what is going on now? What are the causes? Can you show me XYZ? What should we do to avoid/achieve ABC? What is the trend in the past 3 years? Is our product doing well?

Depending on the industry, the data analyst could go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst). Regardless of the title, a data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions.

Data Engineer:

How do data analysts and scientists get the data? How does the data come from user behavior to the database? How do we make sure the data is accountable? The answer is data engineers. Data consumers cannot perform their work without having data engineers set up the whole structure.

A data engineer develops and maintains data architecture. They are experts at preparing massive databases so that analysts and scientists may use them. They are entrusted with designing, constructing, testing, integrating, managing, and optimizing data from a range of sources and are focused on the production readiness of raw data and elements such as formats, resilience, scaling, data storage, and security. Where an analyst must understand data, an engineer must create programs that can transform data into a useful layout. Every business relies on its data to be accurate and available to those who need to use it. The data engineer ensures that all data is received, transformed, stored, and made available to other users in a timely manner. Their primary focus is to build free-flowing data pipelines by combining a variety of big data technologies that enable real-time analytics. Data engineers also write complex queries to ensure that data is easily accessible. To put it simply, everything that happens to the data before reaching the database is taken care of by data engineers.

Data Scientist:

While Data Science is still in its infancy, it has spread throughout practically every industry sector. Businesses are searching for data scientists to help them improve their performance and productivity. Companies use data to study and learn about different trends and practices. To accomplish this, they hire data scientists with particular knowledge of statistical tools and programming expertise. Furthermore, a data scientist is familiar with machine learning algorithms. These algorithms are responsible for predicting future events.

A data scientist is a person who can add significant value by solving more open-ended questions and leveraging their sophisticated statistical and algorithmic understanding. If the analyst is concerned with comprehending data from both past and present perspectives, the scientist is concerned with making accurate forecasts for the future. They conduct online experiments, formulate hypotheses, and uncover trends and forecasts for the organization using their understanding of statistics, data analytics, data visualization, and machine learning algorithms (data prediction). They also work with corporate executives to understand their special needs and communicate complex findings in a way that a general business audience can grasp, both verbally and visually.

Data scientists also build products like recommendation systems that predict what you like, a ranking system that predicts the order of popularity, and NLP systems that predict what a sentence means. Data scientists build these products not to help make business decisions, but to solve business problems.

Skill Set

Data collecting, handling, and processing are the core skills of a data analyst. A data engineer, on the other hand, requires an intermediate level of programming knowledge as well as a grasp of statistics and math to design comprehensive algorithms. Finally, a data scientist must be an expert in both fields. Machine Learning and Deep Learning require data, statistics, and math, as well as in-depth programming knowledge.