Data Scientist Vs Data Engineer, who companies need?

We all must have heard or read somewhere that Data Scientist is one of the most lucrative job of 21st century. Well what about Data Engineer ? When you put this question in open forum some people say , yeah they are almost same. Few go to the extent saying Data Engineer = Data Scientist - R (language).

Well after spending years, working with different Data platform along with Data Engineers and Scientists here is my understanding on differences and commonalities .... hmm rather I would say how these both compliment each other and coexist.

Data Scientist

Data scientists aren't like every other scientist. What differs them from most of us is that they are math experts. They use linear algebra and multivariable calculus to create new insight from existing data.

Data Engineer

Data Engineers are the link between the management's big data strategy and the data scientists that need to work with data. What they do is building the platforms that enable data scientists to do their magic. To create big data platforms the engineer needs to be an expert in specifying, setting up and maintaining big data technologies like: Hadoop, Spark, HBase, Cassandra, MongoDB, Kafka, Redis and more. What they also need is experience on how to deploy systems on cloud infrastructure.

Who Companies Need

For a good company it is absolutely important to get well trained data engineers and data scientists. Think of the data scientist as the professional race car driver. A fit athlete with talent and driving skills like you have never seen.

What he needs to win races is someone who will provide him the perfect race car to drive. That's what the solution architect is for.

Like the driver and his team the data scientist and the data engineer need to work closely together. They need to know the different big data tools inside and out.