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Why Data Engineering and MLOps will be the future of IT Recruitment 

By Vahid Haghzare, Director Silicon Valley Associates Recruitment &
Armie Garcia, Marketing Associate, Silicon Valley Associates Recruitment
 

 


For most organizations, data is the new centerpiece of their operations, meaning Data Science, Data Engineering, and MLOps are the promising positions of the near future for IT Recruiters.

In a recent market research study, the global big data and data engineering services market is presumed to more than double to $77.37 billion by 2023 (from $29.50 billion in 2017)- an annual growth rate of 17.6%, and this will translate into numerous remits in the world of IT Recruitment for the next few years in Hong Kong, Singapore, Shanghai, and rest of Asia.

There is no doubt the in-demand positions in the years to come are Data Engineering, Data Science, and Machine Learning Operations (MLOps). Data Engineering is just one component of the complicated MLOps, the other parts include Data Science, Artificial Intelligence (AI), Machine Learning (ML), and DevOps. 

But if you thought it's only IT companies that are adopting data practices, think again. 

Silicon Valley Associates Recruitment has clients this year in the financial industry, hospitality industry, travel, food & beverage, leisure, and transportation; just some of the many sectors leading the embrace of data in creating smart strategies for their businesses.

In this article, SVA Recruitment will tackle explaining why Data Engineering and MLOps in particular, will be the fastest-growing employment niche within the Software Development world.


Data Engineer: its role & expansion

With the continuous increasing usage of advanced technology like mobile devices, AI, and cyberspace; Data Engineering skills are now the most in-demand in the tech market today and will continue to increase in the future.

Data Engineering correlates with Data Science. Data Engineers collect, maintain, and clean up the data- prioritizing the preparing of structured and unstructured data from various sources. Data Scientists apply models on top of the clean data sets, which creates operational models which businesses can then build on. and identify the applicable sequences. Data Analysts also use the clean data by the Data Engineers to analyze the substantial sets of data and create statistical analysis.


A near future with Machine Learning Operations (MLOps)

MLS is a set of practices inspired by DevOps, that incorporates machine learning, Data Science, and Data Engineering; the target being to install and keep ML systems in production valid and coherent. 

Organizations’ have been forced to expedite digital transformation projects to start the ‘new normal’ of following virtual/work-from-home safety protocols. To successfully implement the new normal, IT firms, companies, and businesses are being required to design highly intelligent systems and applications, and MLOps is key to fulfilling this. 

For businesses, MLOps is the advanced way to unlock loads of untapped data, lower manpower costs, save time, and create more fluid operations, intelligent systems, and a receptive customer experience. As AI and ML become more and more economic and accessible over time to wider audiences, more Companies and businesses will need skillful and knowledgeable workers to operate and manage these systems for them, making it another high-demand job in the market.

But this goes beyond just Engineers and IT employees. Rather than it just being the job of the Data or IT team, the entire workforce of a company- from accountants to sales and marketing- will likely in the future start to be trained and upskilled in how to clean data, handle and analyze big size data sets using PL / SQL, Python statistics and linear regression, and tools such as Snowflake and Grafana.  

Data Scientist must ultimately work closely and efficiently with data engineering and ITOps to manifest MLOps, with teams across the organizations needing to work collaboratively and communicate well with each other- rather than silos- to help streamline the end-to-end process of data collection, cleaning, building models and pushing them into production. 


Data Engineering and MLOps talent crucial for future organization success

With using numerous sources now able to gather masses of data, organizations should utilize and compete in the succeeding years with these data to provide better Customer Experience, streamline engineering operations, and improve decision models. 

This is why Data Engineering and MLOps are vital parts for companies and businesses in the next few years. 

Investing in these tech workers is an ideal move for the company to fabricate highly intelligent systems and create good opportunities for them.

But relying on these Data employees is not enough. Upskilling employees in other areas of your organization is also vital to create a streamlined process from when the data is collected all the way to your Scientists eventually building the models on top, to put the data to work for you. 

 

Visit our Job page for more Job opportunities and the Current Candidate page for available candidates.

 

   
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Silicon Valley Associates is ideally positioned to support the continual demand from tech companies and IT Departments looking to hire in Hong Kong, Asia, and Worldwide. Please let us know if you would further advise on the above topic or your hiring needs

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