Key Skills needed to be a Big Data Engineer

Big data engineers are professionals who provide contextual analysis to the massive amounts of real-time data that can be generated from a particular organization as actionable intelligence that acts as a major compass for the direction the company wishes to move toward. The data provided is provided from several sources, including transactions logs, sensors, social media platforms, log data and GPS plot points. Their goal is to identify and

Produce meaningful and predictive insight that have huge influence on business decisions which are used with the intension of optimizing and developing the overall landscape and operations of the business. With technological advancement, we have access to higher internet speeds and even greater processing power which allows us to develop innovative tools for analysis of data and simple, widespread use of cloud-oriented solutions. The technical benefits of having a data science division at an organization has made it one of the most promising and lucrative IT careers, with an estimated average earning package of $85,000-$90,000. The earning capacity of the data science professional increases as the he/she diversifies and/or specializes within the industry, according to their own strengths.

Here are some of the skills required and daily responsibilities of data scientists-

  • Extensive data mining, creating models, and hypotheses are among tasks which are in direct support of high-level business goals.
  • Up to date with the latest tools for analysis, machine learning, modelling, etc. if they are to be successful in such a dynamic field.
  • Most data engineers have a strong technical educational background in statistics, business, mathematics, and computer science. Alongside their foundational education, many individuals opt for data science certifications as they greatly add value to the skillset of an employee.
  • Communication skills must be advanced enough to be able to process and present data driven context, both visually, and verbally.
  • Many data scientists utilize custom algorithms to determine incomplete data groups and help solve analytical problems.
  • Big data engineers use extensive presentation skills for their expertise to be effective. Relevant information must be acquired from legitimate sources and present in the form of visualizations that are relevant and easy to understand.
  • Many data scientists use Hadoop – an open-source Apache framework – to analyze & mine big data sets.
  • Extensive machine learning knowledge allows data scientists to use SQL, Python, Unix, PHP, R and Java – which they use to modify or develop custom analytical solutions.
  • Data scientists must be capable of operating in a team setting of variable composition, consisting of with managers, IT administrators, programmers, statisticians, graphic designers, and experts in the company’s products or services.

Educational Foundations

What makes this profession hard to pursue is the placement of multiple barriers of entry as this profession demands a great deal of expertise from the professional. It has one of the steepest list of qualifications and skills to be able to pursue a career in data science. Over 40% of data scientist positions require advanced technical degrees like M.A., Ph.D., or even M.B.A. Many companies also accept associated professional undergraduate courses like Computer Science, Mathematics, Information Management, Engineering, etc. Schools can also provide career-oriented programs- degrees and certificates in analytics-oriented disciplines like database management, predictive analytics, business intelligence, big data analysis and data mining, all of which provide a solid foundation for a career in data science.


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