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How to Hire a Data Engineer on the Hot Job Market: Expert Hiring Workflow

How to Hire a Data Engineer on the Hot Job Market: Expert Hiring Workflow

​​The job market for Data Engineers is hot. With the rise of machine learning (ML), artificial intelligence (AI), and Internet of Things (IoT) technologies, the demand for skilled Data Engineers grows because of the skills they bring to the table, helping companies make sense of the huge data arrays they receive and generate daily.

In this article, we’ll walk you through the expert hiring workflow that Bridge’s team uses when we’re sourcing developers for our customers. We will share the talent sourcing strategy we developed for Obvious.ly and cover everything, from defining your ideal candidate profile and writing a job description that will attract them, to onboarding your best-fit candidates. Let’s get started. 

Why do companies struggle to find Data Engineers? 

Data Engineers are in high demand, and the job market is competitive. Companies are struggling to find Data Engineers with the experience and skills they need to provide value, which is why we’re seeing so many hiring roadblocks. Below are the main Data Engineer recruitment challenges companies face. 

  • Data Engineering is a relatively new field. Being the new kid on the block, there are not so many educational programs globally related to Data Engineering. Public and private educational institutions worldwide work on developing Data Science and Data Engineering educational programs to meet the growing demand of businesses, but they still lack a clear understanding of what kind of skills companies need today and most importantly, what skill set they will need tomorrow. 
  • The misconception between Data Science and Data Engineering. To clarify, Data Science specialists work with unstructured data, cleaning and organizing it. Data Engineers, in turn, develop, test and maintain data structures, that is, databases. However, these two roles often get confused and interpreted as the same, which results in companies looking for a wrong specialist that they don’t need.
  • Changing technology. Being a new tech field, technologies related to working with data are rapidly changing and evolving. To stay competitive in the labor market, businesses tend to develop their solutions using the latest technologies, but it is hard to find a Data Engineer proficient in technology that just appeared. 
  • Growing demand for Data Engineers. According to the Dice report, Data Engineer is the fastest-growing tech occupation showing 50% year-after-year growth. SQL, Java, and Python (the technologies Data Engineers should be skilled with) are the hottest tech skills employees globally are looking for. 
  • High salary expectations. Another challenge companies face when hiring Data Engineers is that qualified candidates often have high salary expectations. The average monthly salary of a Data Engineer in the US is $9500 — and those numbers are only going up!
  • Burnout. According to data.world and DataKitchen survey cited by Business Wire, 97% of Data Engineers surveyed experience burnout because of the number of routine tasks they have to do manually and no DataOps practices in place. As usual, burnout leads to decreased productivity, and a low retention rate, making companies launch new hiring campaigns again and again. 
  • The Great Resignation. Because of the reasons above, 79% of Data Engineers surveyed considered leaving the industry, and 70% – changing the workplace in the next year. 

However, there is still good news. The talent pool of Data Engineers continues to grow. Now, there are almost 250,000 Data Engineers’ profiles on LinkedIn in the US only. So, let’s find out how to hire them. 

How to hire Data Engineers (Bridge’s experience)

Let’s discover how to hire a Data Engineer following the case of one of our customers, Obvious.ly. We bet you have heard about this company – it is one of the most known influencer marketing agencies worldwide. They reached out to us with the request to hire Data Analysts and Data Scientists who, in turn, will work on their proprietary data platform that allows the customers to track their marketing campaign results. 

Our talent sourcing strategy allowed us to source up to 10 qualified candidates per week, and here are the main steps we’d like to share so that you’ll be able to get the same results. 

Decide on the talent pool

Data Engineering skills are specific and in most cases, difficult to find locally. That’s why companies may lack an understanding of where to hire software developers with the required background, skillset, and experience. According to Statista, the Czech Republic, Hungary, Poland, Ukraine, and Romania rank best in Data Engineering skills worldwide. 

Our experience also showed that the Latin American region is the most promising destination for hiring specialists in Data Science and Data Engineering. When we were sourcing talent for Obvious.ly, we focused primarily on the LATAM tech talent market, which brought very fruitful results.

IT Outsourcing in Latin America – Discovering the Opportunities

Define the skills you need and create a job requisition

Before you hire a Data Engineer, you have to understand and shortlist Data Engineer skills you need. Below is the list of Data Engineer’s technical skills that are required and preferred:

Based on the skills you have defined, create a job requisition. You are welcome to add project-specific requirements as well. For example, when we were creating a job requisition for Obvious.ly, we also specified the desired Ruby background, statistical programming skills, and salary expectations to help the company meet its hiring budget. 

Below are some tips on how you can create an effective job requisition.

  • Be specific with the technologies your candidates should be skilled with. To get the right candidates, this is a must. The more detailed list of the tools and technologies you provide, the more fitting candidates you will be able to attract, avoiding the need to re-train them. 
  • Highlight the Data Engineer’s responsibilities. This will give applicants an idea of what it would be like to work on your project, which can be helpful when deciding whether or not they want to apply for the job.
  • Tell more about your project and prove its value to the applicants. In this way, you can show why candidates should work for your company specifically — by sharing your values you will let applicants know why your project matters!

How to Attract Tech Talent: What Matters Most to Software Developers?

Use data-driven solutions to build a strong talent pool

If you are looking for a Data Engineer, you probably want them to help you get more data-driven insights. So, why not use the power of data when it comes to finding and hiring them? Fortunately, there are lots of advanced solutions that you are welcome to use at each of the hiring steps — from distributing your job requisition to onboarding your candidates. 


Artificial Intelligence to Help You Source and Hire Tech Talent

When sourcing Data Engineering talent for Obvious.ly, we used our proprietary data-driven engine. It was developed for sourcing technical talent specifically, helping us make the best-validated choices in a 900k-wide pool of candidates. Next, our human talent sourcers vetted potential hires, ensured cultural fit, and compiled a list of the best-matched applicants.

Develop outreach campaigns and test them out

Reaching out to specialists with unique and niche-specific skills is difficult. They get a lot of offers frequently so your message can easily go unnoticed. At this point, our local recruiters suggest being smart and creative – for example, write the first outreach message in two languages that are specified as native in the candidate’s profile. 

Then, you have to come up with different outreach messages and A/B test them to find out which one works best for your candidates. And instead of sending them manually, consider setting up an automatic mailing. 

Invite the best-fit candidates to the interview

After you have gathered a database of suitable candidates, shortlist the ones that fit your requirements more and schedule the first interviews with them. The goal of this stage is to get to know the candidates personally and select the ones you are most comfortable with. 

When we have sourced the candidates to Obvious.ly, we submitted the best candidates to the client via a Slack channel. The customer was satisfied with the quality of the candidates we provided and proceeded with interviewing them. 

Run a technical interview

The technical interview is an opportunity to assess the candidate’s technical knowledge and their approach to problem-solving. In this step, you have to focus on algorithms, data structures, and system design. The technical interview should be based on project-specific scenarios. 

The idea here is not to just have a conversation about your company or its products — you’re trying to assess whether the candidate’s knowledge, skills, background, and experience match your company’s needs. Since technical interview questions for Data Engineers are highly specific, it is better to either hire a third-party technical consultant or ask an in-house Data Science specialist to help.

When talking about our personal case, the candidates we sourced for Obvious.ly were further interviewed by the agency’s Senior Data Product Manager. 

Hire and onboard the best candidates

One of the most important steps in hiring a Data Engineer is the onboarding process. They are hired to perform certain tasks, true, but they must also be able to work within your company’s unique culture. It’s important that new hires have enough time to get up to speed and learn about the company’s goals, processes, and culture. You are also welcome to discover seamless onboarding tips and practices in our dedicated article

Hire Data Engineers five times faster with Bridge

The process of sourcing and hiring Data Engineers doesn’t differ much from the process of recruiting other IT specialists. But given that Data Engineering skills are highly specific and deeply technical, making the right choice among thousands of candidates becomes difficult, especially if you have no tech background. 

This is where our advanced sourcing-as-a-service solution comes into play. It allows building a talent pool of experts that fit your unique requirements, narrowing your search to the required skill set, location, and salary expectations. Thanks to our tech-enabled talent sourcing, our talent sourcers managed not only to help Obvious.ly find the required specialists but also accelerated the hiring process up to 5 times and saved 60% of the recruitment budget. 

At Bridge, we make catching rare fish easier. Contact us today to hire the finest-tuned Data Engineers at no extra effort! 


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