Data scientist, Data analyst or Data engineer?

What suits you best: Data scientist, Data analyst or Data engineer?


by Aishwarya Banik


January 19, 2022

Data scientist is an ever-evolving role that aspirants must constantly shape their skills

Data has always been essential in any decision-making. Today’s world is completely data-driven, and no business could thrive without data-driven strategic planning and decision-making. Because of its important information and trust, data is used in a variety of jobs in business today. We will be looking at the important distinctions and similarities between a data analyst, data engineer, and data scientist in this article.

Data Analyst

By collecting data, analyzing it to answer questions, and relaying the answers to help make business choices, data analysts add value to their business. Cleaning data, performing analysis, and developing data visualizations are all common tasks performed by data analysts. The data analyst may have a varied title depending on the industry. The data analyst, regardless of title, is a generalist who can work in a variety of roles and teams to help others make better data-driven decisions.

A data analyst may have the ability to transform a business enterprise into a data-driven enterprise. Their primary role is to help others track their development and maximize their attention. While many data analyst roles are considered “starters” in the broader data realm, not all analysts are. Data analysts are crucial for companies with separate technology and business teams, as they are excellent communicators with technical tool skills. A good data analyst will take the uncertainty out of business choices and help the whole business succeed. By evaluating fresh data, merging various reports and communicating the results, the data analyst should be an effective bridge between different teams.

Data analysts often earn less than data scientists or data analysts because they are the most entry-level of the “big three” data jobs. Skilled data analysts from top companies, on the other hand, could earn significantly more. In April 2021, senior data analysts at companies like Facebook and Target earned around $130,000. Stock options and other non-salary compensation are sometimes included in data positions, including data analyst employment.

The data analyst can then use a bespoke API created by the developer to extract a new set of data and begin uncovering interesting patterns in the data and conducting anomaly studies. The analyst will summarize and directly present their findings that their non-technical teammates can understand.

Data Scientists

A data scientist is someone who has a good understanding of statistics and machine learning and uses these talents to make projections and answer questions on a variety of business challenges. People frequently confuse data scientists with data analysts. A data scientist can clean and analyze data. He is an expert in all of these areas and can educate others and develop additional machine learning models.

By tackling increasingly open problems and using their understanding of complex statistics and algorithms, the data scientist can bring tremendous value. If the analyst is concerned with understanding data from both the past and the present, the scientist is concerned with making accurate predictions for the future. The data scientist will use supervised (e.g. categorization, regression) and unstructured (e.g. clustering, artificial neural, anomaly detection) learning approaches in their deep learning models to find hidden insights. They develop computer equations that will help them better recognize trends and make accurate predictions.

Since the importance of data science varies wildly from company to company, data science salaries can be quite variable. As of April 2021, senior data analysts at companies like Twitter earned around $178,000 a year. Machine learning engineer positions, which pay an average of $149,924 per year in the United States as of April 2021, are ideal for data scientists who want to develop their machine learning capabilities.

To gain deeper insights, the data scientist would most likely expand on the analyst’s original findings and study. The data scientist will bring completely new insight into not only what has gone before, but also what may be conceivable shortly, whether by training machine learning algorithms or performing sophisticated statistical analysis.

Data Engineer

Data engineers create and improve the platforms that data scientists and analysts use to do their jobs. Every business relies on its information to be reliable and available to those who need it. The Data Engineer ensures that all data is received, converted, stored and made available to other users promptly.

The Data Engineer creates the foundation that data analysts and scientists can build upon. Data engineers are responsible for building data pipelines and typically have to use complex tools and methods to handle huge volumes of data. Unlike the previous two career paths, data engineering is primarily based on application development skills. In larger companies, data engineers may specialize in a variety of areas, including data technology, database maintenance, and designing and managing data pipelines. A skilled data engineer frees up a data scientist or analyst to focus on solving analytical problems rather than transporting data from one source to another, regardless of the topic. The mindset of a data engineer is typically one of development and optimization.

Because data engineers are in high demand right now, they have the highest average compensation of the three professions. According to Indeed.com, the typical data engineer in the United States earns $130,287 per year with an annual bonus of $5,000 as of April 7, 2021. Data engineers in top organizations with lots of experience can earn a lot more. In April 2021, senior data engineers at Netflix, for example, were making over $300,000 a year.

Back-end, the Data Engineer is constantly improving analytics solutions to ensure that the data the business relies on is accurate and readily available. They will use a variety of technologies to ensure data is managed appropriately and relevant information is available to anyone who needs it. A good data engineer saves a lot of time and effort for the rest of the company.

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Sean N. Ayres