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The Internet of Things and other various channels through which information and consumer data can be captured is on the rise, as a result, phenomenal amounts of data are being captured every second. Big Data has great insights which can be used to enhance end-user experience and aid business growth. It is established that extracting information out of this complex web of information is the tricky part, which has in turn given rise to new departments with specific skills targeted in getting the order out of chaos, across organisations.
Initially, any professional working in the capacity of big data, in the data science department was called a data scientist. Over the last few years, as the field is getting more complex and advanced and with the diverse possibilities, one can see companies hire specific professionals under various titles, like, Data Engineers, Data Architects, Business Analyst, Data Analyst, Machine Learning Engineers, Big Data Engineers, etc…,
Let us get a quick overview of the common roles within the Big Data scope and a general understanding of their responsibilities.
This role is a branch out of the Data Scientist role, most organisations also call it the Junior Data Scientist. They play a vast role in the entire data science life cycle, from acquiring massive amounts of data to processing it, analysing it and also summarising the findings. They need to be involved in data scrapping and maintaining the quality of data. The skill set of a data analyst is vast, from knowledge of SQL to programming languages like Python, R, SAS, proficiency in statistics, math, techniques like data mining, data warehousing, and data visualisation, designing and deploying algorithms, knowledge of extrapolating data using advanced computer modelling, fixing code issues and pruning data, can be described as a day’s work of a data analyst.
A data scientist does all that a data analyst is expected to do, however in terms of scope of the role, a data scientist has more responsibilities and is expected to have greater knowledge. Referred to as the sexiest job of the 21st century, a data scientist is perhaps the most in-demand role. Besides being a pro in R, SAS, Python SQL, they mostly have a higher degree in quantitative subjects like math or statistics and should be great with big data analytical tools and technologies. They need to be up to date with not only the technical know-how but are also expected to be proficient with the business know-how of the industry they are associated with. They become the bridge between understanding the business challenges, adding value by deriving insights from the data, using predictive analysis and communicating the finding to the business stakeholders so that data-driven decision making, based on models and prototypes can be done.
Data Engineers/ Data Architect
They are the builders and managers of the big data infrastructure; they are responsible for making sure that the big data ecosystem is functioning smoothly. The essentially build, test and maintain data management systems. Their role is not to arrive at any solution for the business, they work on the linear path to ensure that existing system can be improved by integrating it with new data management technologies. Besides being programming wizards they also must have knowledge of ETL tools, API’s, data modelling, data warehousing etc…,
Machine Learning Engineers
Data science is coined as the most promising profession of the century, people who are skilled in data literacy and strategic thinking, are inquisitive, who have the ability to look at data and spot trends, are key professionals to any organisation dealing with Big Data. the roles can be overlapping but exciting nevertheless.
Based on your skill sets and interest you can identify the role most suited to you, or you can pick up one of data analytics course to build the skill set to become more viable.