Categories: Big Data

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 doing in the school/college.

 

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

2 months ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

3 months ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

3 months ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

3 months ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

3 months ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

3 months ago