9 ways to tackle the Data Engineering talent shortage

There is no doubt that data engineering is one of the most in-demand skills right now. As companies compete to provide better services and experiences, they need data engineers to make sure all of their systems are functioning correctly. But finding qualified candidates for these roles can be challenging due to the shortage of professionals with these skillsets. Companies are getting creative in how they source talent — from internships, on-the-job training, and apprenticeships to online courses and advanced degrees — but there is still a lot left to be done.

The data engineering talent shortage is an industry-wide problem. To solve it, organizations need to rethink their approach to recruiting, build the right team culture, and create a sustainable career path for their data engineers.

The companies that win in data-driven industries are the ones with a unified approach to data engineering talent. This strategy starts with a senior leader who recognizes the importance of data engineering, not just as a complement to other roles but as a strategic asset. This is followed by hiring and development plans that enable the organization to handle growth, peak demand, and attrition. Then you must ensure that your data engineers have the right tools for the job: efficient code practices, personal workstation choices, and platforms that enable rapid development. The final key is providing opportunities for growth; this requires both training programs (from self-study through live classes) and peer mentorship through communities like Meetup groups and conferences.

The data engineering talent shortage is an extreme problem for the industry, and it’s only going to get worse before it gets better. In this article, I share the 9 ways you tackle shortage of talented data engineers (most of these tips are not technical) and talk about how to recruit management, in addition to hardware and software engineering roles.

  1. Low-code/no-code: A DataOps enabler tool like StreamSets can help non-programmers in Data Analysts and Data Science teams to pick up the task of extracting and transforming data when they need. It creates a bigger and wider team and also makes Data Engineers more productive.
  2. Centre of Excellence: Setting up an organisation-wide Data Engineering Centre of Excellence or Practice will help bring data enthusiastic people from around the organisation in one place. The CoE will play a vital role in setting up standards, patterns, and help in recruitment and training.
  3. Internship programs: Setting up an internship program will help brew talent. It provides young and enthusiastic resources that are hungry to learn new things. These resources can be trained on the tools and technology that are needed by the organization to deliver business objectives using data.
  4. Data Virtualisation tool: Implementing and using a data virtualization tool like Denodo can help democratisation of data. The end users don’t need to worry about 10s of different systems to get access to and also don’t need learn ways to extract/load data to these systems. Data virtualisation creates an abstract layer over data sources. The end-user uses SQL-like code to extract data from the views in the Data Virtualisation tool and in the background the tool handles extraction and loading.
  5. Meetups and Conferences: Sending your data resources to top meetups and conferences will enable them to learn about how other organisations are using the tools and technology to deliver business value. Presenting at some prestigious events also showcases the work done by your organization and attracts talent.
  6. Training and Certifications: It is one of the best way to immerse yourself into data engineering and acquire essential skills you need to work with a range of tools and databases to design, deploy, and manage structured and unstructured data.
  7. Consultancy: Working with a consulting company like Kermit Tech to source well trained and talented data engineers to fulfil the requirement in short term is a good idea. Possibility of directly hiring the consultants after the cool down period is another good way to out-source the searching and training of the resources to the experts and get the resources that can be productive on day #1.
  8. Recruitment: Using a long list of items on job description is not a good idea. It is difficult to find a resource that satisfies all the criteria that you are looking for. In the prospective candidate, look for the attitude towards learning, team fit, organisation culture fit, and good communication skills. Rest all will come along.
  9. Mentorship: Signing up for mentorship programs with local universities and other organisations can help attracting talent and also gives you an opportunity to give back to the community. Inviting students to your organisation to give them a taste of corporate world and real life experience being a Data Engineer can help them decide their career path.

Unfortunately, there is no magic bullet or one solution fits all kind of formula to solve the Data Engineering shortage but looking at the above points will help grow and attract talent through both traditional and non-traditional methods.

Application development vector created by upklyak – www.freepik.com