big data career path_2

What job opportunities are there in a big data? 

Big data has become a buzzworthy phrase, and for good reason. You may have read that there is a ‘huge shortage’ in big data and data science job roles or that the demand for big data professionals is high. But that couldn’t be farther from the truth. There are a few different career paths available in big data and data science, but which role in a big data career path would you consider?

Big data is a big industry, and you don’t simply fall into a big data career path. Big data is the application of data science, where huge data sets require overcoming complex but logistical challenges. The main priority is to efficiently capture, store, extract, process, and analyse information from these huge data sets.

Special techniques and tools (e.g. algorithms and software) are required to process and analyse the data sets. Without these tools, it wouldn’t be feasible or achievable due to computational limitations.

In this post, we’ve summarised some of the jobs you can do under a big data career path as a big data professional. Each role requires different skills and responsibilities across a number of seniority levels.

Data Management professional

This can be categorised as an IT role, which is similar to a database administrator. The responsibilities are in the name, the priority is to manage the data and the supporting infrastructure. As a data management professional, minimum data analysis is required, and languages such as Python and R are likely not necessary.

Basic language tools such as SQL may be used, as well as Hadoop-related query languages such as Hive or Pig.

  • Important technologies and skills to focus on:
    • Apache Hadoop & the ecosystem
    • Apache Spark and the ecosystem
    • SQL & relational databases
    • NoSQL databases

Data Engineer

Taking the big data career path as a Data Engineer means you won’t directly deal with analytics. The data infrastructure (managed by the data management professional) needs to be designed and implemented by the data engineer. Without one professional or the other, the data infrastructure wouldn’t exist.

The technologies and skills required for both a data manager and a data engineer are similar, however, they will require different levels of understanding.

Business Analyst

As a Business Analyst, the role requires interacting with or querying of databases, both on a relational and non-relational level.

The priorities of a business analyst are to pull information from the data as presented. This can be compared with the following two roles: machine learning researcher and the data-oriented professional. These two roles focus on producing insight from data further than what can be comprehended on the surface. As business analysts, this entails a unique set of skills among the roles offered.

  • Important technologies and skills to focus on:
    • SQL & relational databases
    • NoSQL databases
    • Commercial reporting and dashboard package know-how
    • Responsive ad-hoc reporting, and sound knowledge of tools for quickly adapting
    • Data warehousing

Machine Learning researcher/practitioner

The role of machine learning researchers/practitioners craft and use specialised analytical and correlative tools to influence the data. Machine learning algorithms let the application of statistical analysis at big speeds, and those who wield algorithms are not content with allowing the data speak for itself in its current state.

The exploration of the data is based on the individual machine learning professional. Additionally, a machine learning professional will have enough statistical experience to know when one has explored sufficiently, and when the responses provided are not reliable.

As a machine learning researcher, the biggest assets that will be valuable are statistics and programming.

  • Important technologies and skills to focus on:
    • Statistics
    • Algebra and calculus (intermediate level for practitioners, advanced for researchers)
    • Programming skills: Python and C++
    • Learning theory (intermediate level for practitioners, advanced for researchers)
    • And finally, a good understanding of the range of machine learning algorithms (i.e. the more algorithms the better, and the deeper the understanding the better)!

Data Scientist

In each individual role, the data scientist is focused primarily on the data. This will involve what the data scientist can interpret based on the data, regardless of what technologies are needed to carry out the task.

The data scientist may use any of the technologies listed in any of the roles listed above, but that would depend on the specific role. The term ‘data scientist’ more often than not, means nothing specific, but everything in general.

Data Scientists are king of the data world and will know or be familiar with the following:

  • Getting the Hadoop ecosystem up and running
  • How to perform queries against the data stored within
  • Extract the data and house in a non-relational database
  • Taking non-relational data and extracting it to a flat file
  • Wrangling data in R or Python
  • Engineering features after some initial exploratory descriptive analysis

So these are the roles you could consider in a big data career path (providing you have the relevant skills). Internet of Things (IoT) is also a part of a big data career path, however, this is a special case of data. These roles relate to IoT data with some adjustments, but the core authenticities remain.

Although there is no definitive skills set for a big data career path, focusing on the mentioned technologies and skills in this post will boost your advantage. As a result, if you are already experienced in these areas, you can feel assured that you will be able to start a big data career path.

What has been your Big Data career experience so far? Let us know the path you’ve taken, and what your thoughts are on the skills required for a Big Data career path. Leave a comment below!