Big Data – Team to Hire for Big Data Practice

This article represents thoughts on Big data team composition and different considerations to make in order to hire and build an effective Big Data team. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.
A Big data team would need to cover following two key areas for becoming an effective team ready to deliver on key Big Data initiatives.

  • Data engineering
  • Data science


Data Engineering Team

You would want to build a team who plays key role in some of the following areas:

  • Data processing (Hadoop Map/Reduce)
  • Data storage (HDFS/HBase)
  • Data coordination (Zookeeper)
  • Data monitoring/management

For above skills, following are different job roles that would match.

  • Hadoop Engineer: This guy should be able to take care of aspects such as data processing, data storage etc.
  • Hadoop/Big Data Admin: This person should be responsible to manage the Hadoop infrastructure.


Data Science Team

Following are key skills that would form the part of job description of a data scientist:

  • Machine learning
  • Mathematics & statistics

To be able to work in above area, the data scientist may be required to one or more of the following tools/libraries:

  • R platform
  • Hive or PIG
  • Java/Python libraries

One may use one of the following approaches in order to build a data science team:

  • Look for the employees within the company having one or both of the above skills or experience with one or above languages. This may be a little tricky as you would be required to arrange for training sessions for these guys to get upto speed with above topics in data science.
  • Hire from outside (lateral hire), a person having both of the above skills or a set of people (two to start with) well versed with machine-learning and maths & statistics skills. This may prove a bit tough and expensive as well. However, it may be good idea to hire at least one senior guy and provide them with a team of two-three junior resources/freshers.
  • Hire a set of freshers (MCAs or Msc Maths/Statistics) who have been doing machine learning as well as mathematics & statistics in their school or college. This may work out well as the freshers should take no time to get up and running as this is what they might have been┬ádoing in the school/college.


Ajitesh Kumar

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