Top 4 Data Analytics Roles To Look Out For

What does a data engineer do? How does that differ from what a data analyst’s role is? Who is a data engineer then?! Confused? Here’s an attempt by us to put these roles into different buckets and answer all these questions for you.

If you are looking to transition from IT, or any other field, to Data Analytics, then you may want to go through this carefully to understand your options. Moreover, if you are already in this field, and are considering a shift or upgrade, then here are all your options mapped out for you!

Data Analytics is an evolving field and although these roles are defined loosely, there are 4 buckets within Data Analytics roles.

Let’s take the example of an e-commerce company, and in the context of this example let’s try and understand all these different types of job roles. Check out the video or infographic or just continue reading below!

Data Engineer


A data engineer creates the platform and the data structure within which all the data from the users is captured. For example, the items they buy, what is in their cart currently as well as on their wish-list. Data engineers should make sure that the captured data is stored in such a fashion that it is not only efficient but also easily retrievable.

They are comfortable in working with varied data sources, write ETL queries to collate data from all of them, and then organize all this data in data warehouses or databases so that others in the company can make the best use of it.

To become a data engineer you need to acquire knowledge of languages such as Python, Java, SQL, Hadoop, Spark, Ruby, and C++. You should note, however, that knowledge of all of these is not mandatory but varies from company to company.

As a data engineer, you would be sitting at the rare intersection of a software engineering professional and a data analyst.

Data Analyst


Data analysts are expected to draw insights from the data, which directly impacts business decisions. Data analysts are directly involved in day-to-day business activities and there are a lot of ad hoc analyses that a data analyst or a business analyst is expected to do.

For example, a data analyst in an e-commerce company helps the marketing team identify the customer segments that require marketing, or the best time to market a certain product, or why the last marketing campaign failed and what to do in the future to prevent such mistakes. Hence, for a data analyst, a good understanding of business, data, and statistics is essential.

The tools and languages that would be most commonly used by a data analyst would be Excel, SQL, and R, and in some cases Tableau as well.

Top 4 Analytics Skills


Data Visualiser/Business Intelligence Professional


There might be a data visualizer or a business intelligence professional at this e-commerce company, who is responsible for creating weekly dashboards to inform the management about weekly sales of different products, the average delivery time, or the number of daily cancellations of orders.

Data Scientist


A data scientist uses the data that the organization holds, to design business-oriented machine learning models.

As a starting point, data scientists can go through the available data of the company to look at various buying patterns, identify similar items on the website, and identify similar users. Then, they will create algorithms around the same so that the website can automatically recommend products to the users based on their navigation histories, purchase histories, etc. This solution must be effective enough that it can predict the future purchases, in real-time, for visitors of the website.

The way this is different from a data analysts’ role is that data analysts are expected to perform a lot of ad hoc analyses which can facilitate decision making within an organisation. Data scientists, on the other hand, not only perform ad hoc analyses and create prototypes, but they also create data products that make intelligent decisions by themselves. This is where machine learning becomes extremely critical.

The requisite tool and concept knowledge for a data scientist is knowledge of algorithms, statistics, mathematics, machine learning, and programming languages such as R, Python, SQL, and Hive. A data scientist should have a business understanding and the aptitude for framing the right questions to ask and find the answers in the available data and then communicate the results effectively to the team members, and all the stakeholders.

Unleashing the Power of Data Analytics


We hope that this helps you segment all the different Data Analytics roles and decide where you fit in best. Good luck for your career!

In case you have any questions, please write to us at or comment below.

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Rohit Sharma

Rohit Sharma

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.