The Emergence of Data Engineers & Data Scientist

Last updated on June 23rd, 2022 at 09:22 am

The Emergence of Data Engineers & Data Scientists

Data engineering and data scientist are job titles which might be new to us in recent times, however, these roles have been around for a while. Traditionally, anyone who would analyse data would be called a Data Analyst, and the person responsible for creating platforms to support the analysis is a Business Developer.
In the world of IT, the data scientist gets more visibility and praise, as they are the ones, extracting vital intelligence from big data and helping organisations make critical decisions with regard to their business swiftly. But it is important to note that the data scientist does not work in isolation, they are not capable of generating valuable information independently, and they need the constant support of Data Engineers. The engineers are the ones designing and maintaining software and platforms that operate the big data pipeline. They set the stage and keep it running.
A Data Engineer is essentially a software engineer with a decent understanding of math and statistics, he should be skilled in data warehousing, designing, data collection transformation and programming. Have the ability to transform and load operations, build data warehousing solutions, and test database architecture. He should be hands on with tools like Hadoop, Spark, Javascript etc.…,
A Data Scientist is someone who is an excellent statistician, with above-average software engineering skills. Should be primarily inquisitive, and have the skills of data visualisation and storytelling along with programming skills. His tasks would essentially be to identify the question and find answers through data, find a correlation between dissimilar data, to be able to tell the findings, hence storytelling ability, and lastly should be hands-on with tools like Julia, Python Programming, data visualisation tools like Qlik view or Tableau.
The description of Data Engineers and Data scientists can be quite obscure, there is an overlap. While these roles still maintain to be distinct data science job roles, they require different skills and experience. Some data scientists can do data engineering, while some data engineers can do data analysis and visualisation as well.
The emergence of big data has opened space for new titles and roles to come into existence. Over the past couple of years, businesses have applied all means to get individuals who have the skills to turn data into gold.
A lot has changed in the way businesses function, earlier a lot of companies were functioning in the physical world, nowadays most businesses function on the digital platform. When a company is mostly functioning online, there is a huge accumulation of data. Data about who is visiting your website, if they are choosing your competitor's website as opposed to yours, and what could be the reason, you also get data about the statistics of the competitor’s target audience, So the possibility of the data accumulation is too big and very fast. The data are screaming information and is noisy beyond comprehension.

In order to find a way in this data, one needs to sort this in two ways,
Firstly, to create a database to process the data and to store it and the second would be the need of people to comprehend the data and know-how to ask relevant questions and research the data in a method that the concerned business can take informed pointers from it. This stored data needs people who know statistics and know how to write code, in order to get insightful information.
Data scientists and Data Engineers are these people; they are the need of the hour. To know how to process data using various platforms, and more importantly, we need them to be around, These people also know how to make sense of the information, and how to analyse it. They don't only plot graphs from data collected from a spreadsheet but also create statistical models that over a period of time affect the business and products with effective ways to increase the revenue.
The data available could be stand but smart and appropriately skilled people are the ones who help find that needle in the haystack.