Data Analyst, Data Engineer or a Data Scientist: Wondering which is the right profile for you?

Data Analyst vs Data Engineer vs Data Scientist

Data is crucial to any business. The world has seen the power of data and the potential it holds to transform organisations and businesses. Data science is a rewarding career both in the terms of top companies hiring for data science based roles and the salary they offer. Data Science jobs are always in demand. Based on a survey conducted by Analytics Insight, there will be 3 million job openings worldwide in data science in 2022.

The interesting part here is that companies are looking to hire for specialized roles rather than look for someone who has a little bit of knowledge of each data science based role. So if you do not want to fall in this category, you need to know what exactly is a Data Analyst vs Data Engineer vs Data Scientist.

Unfortunately, people think that each of these roles is similar and require the same skill set. But in this article, I’d like to take you through the Data Analyst vs Data Engineer vs Data Scientist profiles so that you do not make a career decision based on the right points. We’ll also talk about the skill set, roles, and responsibilities for each of these jobs, along with the average salary and top companies hiring for these roles. 

For deeper insights into the world of data science, download the free data science career guide that carries global salary trends, in-demand data science skills and tools, the career paths and job opportunities in various industries and a lot more.

By the end of the article, you’ll know exactly which is the right profile for you and you’ll be able to commence your journey in building a career in the field of data science.

Let’s begin! 

Who is a Data Analyst, their job responsibilities, annual salary?

A data analyst extracts meaningful information from a large pool of data. They gather data, analyze the trends in the market, identify the requirements of their clients, and make data-driven decisions for the organization’s success. 

Most of the professionals who want to enter the data science domain choose this profile. The skill set required for a data analyst includes data warehousing, Adobe & Google analytics, basic knowledge of programming and statistics, data visualization, SQL, MS Excel. The job responsibilities of a data analyst include data representation through reporting and visualization, ensuring data acquisition and maintenance, and optimizing the statistical efficiency of data.

The average base salary of a data analyst in the US is $75,171 per year (Indeed: 4.6k salaries reported, updated on February 12, 2021).

Top companies that hire for data analyst roles include Oracle, Walmart, VISA, etc.

Who is a Data Engineer, their job responsibilities, annual salary?

A data engineer forms a bridge between data analysts and data scientists. They are the ones responsible for preparing data. They need a strong technical background so that they can create and integrate different application interfaces. 

As they have to work with structured and unstructured data, a data engineer needs to have an in-depth understanding of SQL and NoSQL databases both, advanced knowledge of programming, data architecture and pipelining, data scripting, reporting, data visualization. The top skill that a data engineer has is the knowledge of big data. They are experts at programming languages like Java, Python, and handling frameworks like Hadoop, Kubernetes, Apache Spark, Yarn, etc. The job role of a data engineer involves creation, testing, and maintenance of complete data architecture. They also deploy statistical models and ensure data accuracy.

The average base salary of a data engineer in the US is $132,531 per year (Indeed: 6k salaries reported, updated on February 12, 2021).

Top companies that hire for data engineer roles include Facebook, Oracle, IBM, Microsoft, Amazon, to name a few.

Who is a Data Scientist, their job responsibilities, annual salary?

A data scientist is the one who is responsible for analyzing, interpreting complex data and organizing big data. If you want to be a data scientist, the skill set needs to be more extensive. You need to be an expert in statistics, math, computer science, and advanced analytics.

A data scientist needs to have strong statistical and analytical skills, knowledge of programming languages like SAS, Python, R. Knowledge of hadoop based analytics, data optimization, and data mining also cover the skill set needed to be a data scientist. In their job roles, they are involved in the strategic planning for data analytics, integrating data and filling the space between stakeholder and customer. They develop models that can operate on big data and carry out data analytics and data optimization with the help of machine learning algorithms and deep learning.

The average base salary of a data engineer in the US is $123,127 per year (Indeed: 4.3k salaries reported, updated at February 12, 2021).

Top companies that hire for data engineer roles include Accenture, Intel, Mastercard, IBM, Microsoft, etc.

Data Analyst vs Data Engineer vs Data Scientist

Summing Up

So, this was all about Data Analyst vs Data Engineer vs Data Scientist. I hope this article gave you the perspective needed to understand the best role for you. 

Now as you know the difference between Data Analyst vs Data Engineer vs Data Scientist, take a step in working towards achieving your dreams of building a lucrative career in data science. 

For this, I recommend that you pursue The Post Graduate Program in Data Science & Business Analytics (PGP-DSBA), offered by one of the leading universities in the US, Texas McCombs and delivered in collaboration with Great Learning. It offers benefits like a dedicated Program manager for answering your academic and non-academic queries, lectures by the award-winning faculty of Texas McCombs at The University of Texas at Austin, mentoring sessions on weekends, and a lot more.

Fill up this short form to get complimentary demo access to the PGP-DSBA program.

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Great Learning Editorial Team
The Great Learning Editorial Staff includes a dynamic team of subject matter experts, instructors, and education professionals who combine their deep industry knowledge with innovative teaching methods. Their mission is to provide learners with the skills and insights needed to excel in their careers, whether through upskilling, reskilling, or transitioning into new fields.

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