Why Data Engineers are More In Demand?
One of the highest in demand professions in the 21st century, that of a Data Engineer with about 50% growth in the year 2020 alone. Ever since the year 2016, the demand for Data Engineers has witnessed rise as compared to the supply, so much so that it has surpassed the demand for data scientists.
The technological changes brought about by the variety, volume and the sheer force with which big data has taken the industry by a storm is unmatched. It was around the year 2011 that big data-fueled organizations like AirBnB and Facebook expanded their search for professionals to fit the job titles of data engineers.
The technological changes brought about by the variety, volume and the sheer force with which big data has taken the industry by a storm is unmatched. It was around the year 2011 that big data-fueled organizations like AirBnB and Facebook expanded their search for professionals to fit the job titles of data engineers.
With the rise and growth of big data, the skill sets of other IT professionals, SQL developers and software engineers failed to meet the kind and extent of software engineering that was required; software engineering that was primarily driven off and based on data. In order to be able to support a data science strategy, functions like data mining, data warehousing, data modeling, data infrastructure, metadata management and data crunching were mere initial needs.
Did you know?
In the year 2019, the Chief Technology Officer of IBM stated that approximately 87% of projects in data science never even step into the production process. It took a considerable amount of time for the market to realize that data scientists, alone, could not do everything. Data engineers are professionals that help decode the nucleus framework to better understand the organization and structure of data in the data warehouse.
Data scientists largely depend on data engineers to obtain trustworthy and helpful data. While data scientists are experts in the field of mathematics, statistics, machine learning techniques and algorithms, data engineers possess special skills in MySQL, SQL, NoSQL, cloud and architecture technologies like scrum and agile. It is likely that both data engineers and scientists could be familiar with coding languages like Python.
Why are data engineers so hard to hire?
- Strict hiring policies
Data engineers like many other such IT professionals, a lot of times, do not typically conform with the norms of mainstream candidates. A lot of them are usually self-taught and acquire better skills than pedigree candidates. However, because many data engineering companies strictly adhere to their hiring policies and only want the ‘right’ candidates with a particular qualification or degree, many skilled and talented self taught professionals are denied opportunities before they can even present their skills. - Remuneration
Because it is not quite clear to the non-technical mind what exactly it is that a data engineer does, their job has been under-estimated and under-compensated for. With the growth of big data and demand for data engineers and their special set of skills, not many companies have been able to keep up their pace with regards to offering them a decent pay. - Lack of designation clarity
Since data scientists are typically viewed as the leaders of a Data Science function and data engineers are often wrongly perceived as data processors, candidates are hesitant to take up jobs that don’t clearly define their role.
