For the previous few years, we now have seen a excessive demand for hiring knowledge scientists amongst organisations. While this demand goes up, it has been seen that as a result of rising use of massive knowledge and the massive chunks of structured and unstructured knowledge, the info engineering roles have seen an increase amongst organisations. Numerous this may very well be attributed to the rising significance of data engineers who “primarily transform data into pipelines for the data science team” and “lay the foundation in most data science projects.”
At current, LinkedIn is exhibiting greater than 29k job opportunities within the function of knowledge engineering. However, in response to the Stitch Benchmark Report, organisations nonetheless face a big scarcity of knowledge engineering expertise and abilities available in the market. In different phrases, it may be mentioned that there was an enormous scarcity of data engineers amongst organisations when in comparison with knowledge scientists.
To get an trade perspective, Analytics India Magazine spoke to a couple consultants on this discipline to grasp the explanations behind the void and the way the trade can handle such points.
Rajiv Kumar, the Managing Director at Microsoft India, believes that contemplating the pandemic has accelerated the digital transformation globally, firms need to undertake digital methods to achieve out to clients, which has exponentially elevated the quantity of knowledge generated.
“Data is one of the most valuable business assets as companies can get deep insights from these data and thus be more agile and efficient in their execution and operations. As a result, in digital data-driven enterprises, the role of data engineers has become crucial in generating insights to make informed business decisions,” mentioned Kumar. “The demand for data engineers with deep expertise is increasing multi-fold, and we expect to have continued growth in demand in the coming years.”
Increasing Demand For Data Engineers
“To solve any mission-critical, complex real-world problems, you inevitably have to account for all types of data, including addressing the challenges of structured and unstructured data, both on-prem and in the cloud. Hence, for data scientists to work their magic, you need data engineers to prepare the data architecture for ingestion, streaming, storage and continual analysis,” mentioned Brian Rasmussen, Global Vice President – Analytics, Machine Learning and Autonomous Data Warehouse at Oracle.
He firmly believes that the necessity to do all of the work utilizing uncooked knowledge is rising, particularly within the massive scale enterprises coping with complicated issues, within the fast-growing info financial system and the rising AI and ML industries. “These trends are resulting in high demand for data engineers,” he added.
Commenting on the present demand, Saurabh Agrawal, Head – Analytics & ML at Lenskart, mentioned, “The key to scaling up analytics and AI is to focus on organising the data well, supporting creating reusable assets. Even Google mission is to organise information and make it accessible to all. While data scientists focus on deriving value from data, data engineers focus on making the ingredients ready.”
Agreeing to that, the founder & chairman of Zaggle, Raj N, said that data engineering is the fastest-growing vocation within the expertise sector right now, with a progress of round 50% year-over-year. The demand is big, and the variety of knowledge engineers has doubled previously yr.
He mentioned, “If you aren’t making your moves based on the data you have, you are missing out on a lot of things. It is necessary to design, manage and optimise the flow of data in an organisation.”
Speaking of the demand, Sarita Digumarti, COO & Co-founder, Jigsaw Academy, additionally added, “Without ready availability of clean data, there is no point in trying to deploy advanced data science algorithms. And that is why the demand for data engineers has increased exponentially over the last five years. As part of our enterprise training programs, we often see that for every program we run on ML or advanced data science, we run a parallel program focused on data engineering.”
“With more and more organisations moving to data-driven business models, the demand for data engineers is only increasing. As data grows, so does the task of building a reliable, scalable infrastructure to manage it. This is where data engineers play a crucial role. Without data engineers, data scientists would be handicapped,” said Himanshu Varshney, CEO and Co-Founder at HashedIn.
Dearth Of Data Engineers
While asking if there’s extra of a market want for knowledge engineers or knowledge scientists, Rasmussen replied that the 2 roles are very interrelated, and there’s excessive demand for each. He mentioned, “A lot of aspirants are looking to train in data science, while at times underestimating the importance of picking up fundamental data engineering skills, which in my opinion is essential to become a sound data professional.” He considers that this pattern over time might result in a relative scarcity of high quality knowledge engineers, as ultimately, each areas of experience are interdependent and massively vital.”
However, the founding father of Zaggle talked about that there’s extra dearth of knowledge engineers than knowledge scientists available in the market, however the variety of job openings for knowledge engineers is sort of much less in comparison with the job openings for knowledge scientists.
On this subject, Digumarti mentioned that there’s a bigger provide hole in data engineering than in knowledge science. It can be true that with rising knowledge sizes and the deployment of AI and ML fashions at scale, the tech experience required to organise and course of knowledge and create sturdy knowledge pipelines has dramatically elevated. “So the talent gap is much larger on the engineering side.”
Commenting on this, Varshney added, “While analytics and insights are critical for delivering business value, many organisations lack the required skill and resources to manage and scale their data in a structured, reliable way.”
The Demand In Future
Talking to trade consultants, it was established that the demand for knowledge engineering roles would solely develop as organisations look to grow to be increasingly more data-driven. In reality, a key issue for the rising demand for knowledge engineers is the emergence of the cloud because the dominant knowledge platform.
As a matter of reality, Rasmussen confirmed that on-premise specialised roles like database administrator, knowledge analyst, knowledge architect and BI developer would evolve into this one crucial function, that of a “modern cloud data engineer.”
“As data scientists unlock more use cases, data engineers will help scale up the impact. There will be 2-3x more demand for data engineers,” added Agrawal.
Adding to Agarwal, Digumarti said that the demand is predicted to steadily enhance in two particular methods — straight, with increasingly more tech positions specifying core knowledge engineering expertise together with database and knowledge warehousing expertise, ML and MLOps, and distributed computing programs.
As per consultants, earlier than the info scientists can use their algorithms and fashions, the info engineers must organise and classify the uncooked knowledge into one thing appropriate. “Data scientists are only the tip of the iceberg; the data engineers form the rest of it. Hence the need for data engineers will only increase in the coming years,” concludes Varshney.
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