Data science has been called the sexiest job of the 21st century by Harvard, but in a surprising turn of events, data engineering may surpass that status. “The shortage of data engineers will be felt even more in 2022,” noted an AIM survey of key trends in AI and data science for 2022. This is due to the increase in the digital transformation after the pandemic and the data explosion that followed it. .
Keep up with the growing demand for data engineers
Historically, data engineers only dealt with distributed systems and Java programming, but now they must leverage AI, ML, and BI to manage data.
“In the past, there hasn’t been enough emphasis on data engineering. Data science was seen as sexier and more ambitious, while data engineering was seen as the dirty part,” said Nidhi Pratapneni, SVP, Product, Analytics & Modeling at Wells Fargo. “But the complexity of data sources leads it to go back and forth in the value chain, but there is more potential to drive growth through it. The better you design the data, the more effective it will be; otherwise , you continue to run in circles and get in the way of streamlining the delivery of analytics solutions.
Called the nerve center of digital strategy by Sriram Narasimhan, Head of Data, Analytics and AI at Cognizant, the demand for data engineers can be seen through recent market statistics.
In 2020, the Dice Tech Job Report said data engineering was the fastest growing tech job, with an expected 50% year-over-year growth in job vacancies. Global job platform, Monster released two recent trends and employment reports showing that “engineer” was the number one job search during WHAT month. Additionally, engineering was among the top sectors likely to grow by 57% in 2022.
Additionally, the annual salary study conducted by AIM Research in June 2021 showed that data engineers had a higher median salary than big data scientists or artificial intelligence engineers, indicating the growth of the industry. importance of the post. The demand for this can be seen with big tech companies like Google, IBM, Cloudera, and SAS launching data engineering certification programs to train and upskill employees. However, they do not have certifications in data science or AI engineering. “With the market for artificial intelligence and machine learning-based solutions expected to reach $1.2 billion by 2023, it is important to consider current and future business needs. To meet new skills data engineers now need, we’ve updated our Data Engineering on Google Cloud learning path.” Google said.
Why is data engineering getting sexier?
Data is everywhere and in various forms, and it needs to be perfected to derive actionable insights. As analytics professionals, data engineers are responsible for generating, cleaning, processing, and storing data in a way that prepares it for analysis. Additionally, they formulate data management architectures for enterprises to democratize access to data and establish efficient pipelines. Essentially, data engineers lay the foundation for data scientists or AI/ML professionals to use data to derive business solutions.
According to Mathangi Sri, VP Data Science & Head of Data at Gojek, the demand for data engineering can be attributed to the emergence of large-scale data. “Each large company today has nearly ten million customers and millions of transactions per day. So, engineering is first needed to make it accessible to real-time models to run,” she explained. “Factors such as data penetration, data sources, the lack of a state-of-the-art system to present it, and data governance become important. is no longer the case today as they can destroy businesses overnight, other businesses wait for failure to occur.
Moreover, this massive data collected by companies is in different formats; structured, semi-structured and unstructured. Nidhi Pratapneni highlighted the role of engineering in dealing with these forms of data today. “With non-quantitative data, how we record, cleanse and textualize the image data is important. Data engineers put together different forms of data,” she contextualized.
The severe shortage
“There is a huge demand for skilled data engineers. Now is a good time to dive into it and seize the opportunity,” said Prasad Srinivasa, Deputy Vice President of Genpact. Obviously, many think so, given that the demand for data engineers has outstripped its supply since 2016, according to Quanthub. However, the shortage of engineers is more severe than that of data scientists, and it only seems to be growing. “In 2021, LinkedIn is posting over 29,000 data engineering job openings as organizations are still facing a significant shortage with not enough data engineering talent in the market,” Sriram Narasimhan said.
Data engineering is a relatively specialized field, as opposed to data science, which is subject to continuous skill improvement and generalized positions. With the growing demand for technical expertise, the talent gap in data engineering continues to grow. Building multidisciplinary teams and encouraging training and development in data engineering seems to be the key to the future.