Trends and tips in hiring Data Engineers for AI

Trends and tips in hiring Data Engineers for AI

It’s worth understanding the broader market trends around data engineering and AI talent, as this context can inform your hiring strategy. Recent industry reports and surveys paint a clear picture: demand is high and still growing, but the talent market is tight and evolving. Here are some notable trends for 2025 and beyond:

• Unprecedented demand and job growth: Roles at the intersection of data and AI are among the fastest-growing in the job market. LinkedIn’s “Jobs on the Rise 2025” analysis found that AI and data-related roles dominate the top 25 fastest-growing positions, fueled by the rapid integration of AI across sectors like finance, healthcare, retail, and more​.

Data engineers feature prominently in these growth trends. The WEF Future of JobsReport confirms that technology roles are expanding: AI/Machine Learning Specialists, Data Analysts/Scientists, and Data Engineers are all projected to see substantial net job growth, unlike certain clerical roles which are in decline. In absolute terms, the WEF report notes a net increase of 78 million jobs by 2030 globally, with much of that growth coming from tech-driven roles​. For employers, this means the competition for skilled data engineers – already tough – is likely to intensify further as more organizations invest in AI and big data initiatives.

• Talent shortage and competitive hiring landscape: Nearly every survey of tech hiring points to a shortage of qualified data engineers relative to demand. For example, analysis by staffing firm Robert Half placed data engineers in the top 15% of in-demand tech roles for 2025​.  They report that 87% of technology leaders are finding it difficult     to secure qualified tech professionals in the current landscape​. This aligns with anecdotal reports – many HR managers find that strong data     engineering candidates often have multiple offers. The talent squeeze is     leading employers to get creative: offering higher salaries, more flexibility, or considering remote/contract talent to fill the gaps​.

If you’re hiring, be prepared to move quickly and put your best offer forward to snag top candidates. Additionally, be open to upskilling internal talent. In fact, 70% of global employers (and 86% in IT-intensive industries) plan to hire staff with new skills to meet emerging needs, but simultaneously 85% are focusing on upskilling their workforce – a dual strategy to address skill gaps. You may find promising data enthusiasts in-house who, with some training, could grow into the data engineer roles you need.

• Rise of  specialized Data Engineering roles: As the field matures, we’re seeing more role specialization within data engineering. Large organizations now distinguish between roles like Data Platform Engineer, Analytics Engineer, ML Engineer, MLOps Engineer, DataOps, etc.

For example, an “Analytics Engineer” focuses on the intersection of data engineering and analytics, often transforming data and curating it for analysts and BI tools (this role often requires strong SQL and knowledge of analytics/business metrics). An “MLOps Engineer” blends data engineering with ML deployment, concentrating on tooling that automates model training and serving. Similarly, a “Real-Time Data Engineer” might specialize in streaming platforms and live data processing. These nuances are reflected in some job postings and resumes – e.g., a candidate might title themselves as Real-time Data Engineer and highlight skills in Kafka and real-time analytics​.

 As an employer, consider whether your needs call for a generalist data engineer or someone with a specific specialty. In many AI teams, a generalist who can adapt is ideal, but in a very specific project (say, building a real-time recommendation engine), a specialist with deep streaming experience could be beneficial. The key is that even these specialists share the core skillset we discussed, just with an extra focus in one area.

• Focus on AI and Big Data skills: Employers increasingly desire data engineers who are conversant in AI-related tools and big data technologies. The WEF 2025 skills outlook specifically highlights “AI and big data” as the number one skill category increasing in importance by 2030.

At In4Matic, we interpret this on the ground as a trend where data engineers who have experience with AI projects (for instance, helping deploy an ML model, or working with data for NLP/computer vision tasks) have an edge.

 Business leaders have recognized that data engineering and AI expertise go hand in hand: you need both to drive innovation. Some job descriptions now explicitly mention knowledge of machine learning concepts or even basic model-building experience as a “nice-to-have” for data engineers, indicating the closer collaboration between data engineering and data science. Meanwhile, big data technology skills (eg. Spark, Hadoop, distributed systems) remain in high demand as data volumes grow.

 A survey of job postings and employer needs found that professionals who can “work across adjacent fields like data engineering and data architecture” in addition to core data science are especially valuable​. The takeaway: versatile data engineers with exposure to AI and big data contexts are hot commodities.

• Emphasis on soft skills and business impact: another subtle trend: companies are putting more weight on soft skills and business acumen in data roles than before.

It’s not enough to be a pipeline-building wizard; companies want engineers who understand the why behind their work. This means hiring managers appreciate candidates who can demonstrate how their work made a difference.

For example, an engineer who can say “I built a data pipeline that enabled our marketing team to increase campaign ROI by 20%” shows business impact awareness. We see that job listings increasingly mention communication and teamwork, and performance reviews for data engineers often include collaboration as a metric. This is in line with the broader workforce trends identified in the WEF report – growing roles (like data engineers) demand higher proficiency in soft skills like resilience and analytical thinking compared to declining roles​. 

So in hiring, be on the lookout for those who not only have the technical chops, but also can connect their work to business objectives and thrive in a team environment.

In light of these trends, we believe at In4Matic that HR and IT managers should adjust their strategies. Expect to court candidates actively (they have options!),consider flexible hiring (remote or contract to tap a wider talent pool), and evaluate candidates holistically (skills + culture fit).

Also, it may be wise to highlight what makes your company attractive. For instance, if you’re working on cutting-edge AI, emphasize that. Top candidates often choose roles that promise learning and growth. Remember, “the opportunity to work with emerging technologies can be a magnet for today's professionals – candidates see working with AI as an opportunity to develop highly sought skills”​.

Offering such opportunities, along with competitive compensation and flexibility, will help you stand out in the race for talent.

Practical tips for identifying the right data engineering candidates

Finding the right data engineer for your AI team involves carefully examining both what’s on paper(resumes, portfolios) and how they perform in the hiring process (interviews, tests). Below are some practical guidelines and tips that we use at In4Matic to zero in on the ideal candidate:

• Screen for relevant project experience: When reviewing resumes, we look for concrete examples of data engineering work. This could be the mention of building a data pipeline, designing a database schema, working with a specific technology (e.g. “Implemented a Spark-based ETL pipeline on AWS to process streaming sensor data”).

Candidates who list generic duties without outcomes (“handled data”, “worked with databases”) may not have deep experience. Look for keywords like ETL, data pipeline, data warehouse, Spark, Kafka, AWS/GCP/Azure, SQL tuning, etc., which signal hands-on skills.

For junior candidates who might not have work experience, projects from school, bootcamps, hackathons, or personal projects on GitHub can demonstrate their abilities. For instance, a junior might showcase a project where they built a small pipeline to gather and analyze public data – that shows initiative and interest.

We believe it’s useful to review their practical project experience and assess their proficiency in tools (Python, SQL, cloud platforms, etc.) to ensure the resume claims align with reality​

• Examine the technical skill set depth: Use the resume and any linked portfolio to verify the technical skills discussed earlier. If a candidate says they know a technology, there should be evidence of how they used it. For example, if they list Apache     Kafka, do they mention what they used it for (e.g. “built a real-time data pipeline with Kafka streaming for e-commerce clickstream data”)?

 
‍Depth matters more than breadth – a long list of tools can be superficial. It’s often better to see a few core technologies with meaningful experience behind them.Certifications (like AWS Certified Data Analytics, Google Professional DataEngineer) can also indicate knowledge, but practical experience usually trumps certificates. 

In4Matic’s tip: Some data engineers maintain a GitHub or personal blog. If provided, take a look. It can give insight into coding style and problem-solving approach. For instance, a candidate might have a GitHub repo of a data pipeline project or a blog post about how they tackled a data cleaning challenge. This not only verifies skills but shows passion for the field.

• Include a  technical exercise or case study: It’s highly recommended to include a technical assessment in your hiring process for data engineers. This could be a take-home assignment or a live coding interview, depending on your style. Aim for something practical: for example, give a messy dataset and ask the candidate to write scripts/queries to transform it into a target schema, or design a simple data model for a given scenario.  Some companies use a pair-programming approach on a small ETL problem.

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The goal is to see how the candidate thinks and codes. Can they write correctSQL? Do they consider edge cases in data? How efficient and clear is their code? A system design interview can also be illuminating for senior candidates. You might ask, “How would you design a data pipeline to handle millions of events per hour and deliver features for an ML model in real-time?” and see how they architect a solution.

Through these exercises, you can gauge their technical proficiency and problem-solving live. Just be sure to make the exercise relevant to the job’s actual work(e.g., focusing on data transformation logic rather than algorithmic trivia). A well-designed case can also test how they handle ambiguity and requirements gathering, which are part of a data engineer’s job.

• Assess soft skills  during interviews: Don’t relegate soft skill assessment to a gut feeling, but actively probe it. In behavioral interviews, ask questions that reveal communication, teamwork, and adaptability.

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For example:

 
• “Tell me about a time you had to explain a complex data issue to a non-technical  colleague. How did you approach it?” – This checks communication clarity.            
     
• “Describe a challenging team project. How did you collaborate with others and what did you learn?” – This gauges teamwork and conflict resolution.       
   
• “Have you ever had a data pipeline fail or deliver bad data? How did you respond and fix it?” – This can show problem-solving under pressure and accountability.        
 
• “What new data  engineering technology or tool have you learned recently?” – This assesses curiosity and continuous learning.           
  
• Listen for specifics in their answers. A good communicator will be able to narrate the situation clearly and focus on their actions and results. Be cautious of candidates who only speak in abstractions or can’t identify any past difficulties – it may indicate lack of experience or self-awareness. Also, consider involving a data scientist or stakeholder  from the business in one of the interview rounds to see how the candidate interacts with roles they’ll work closely with.       
• Check references   and past impact: When you get to reference checks, ask former managers or colleagues  not just about the candidate’s technical skills, but how they functioned on a team. Questions like “How did they handle tight deadlines or changing requirements?” or “Can you give an example of the candidate’s impact on a project?” can yield valuable insights. Often, past performance in soft areas (reliability, teamwork, learning ability) is a good predictor of future performance. A reference might reveal, for example, that the candidate took initiative to improve a process, or conversely, struggled when requirements changed – insights you wouldn’t get from resume or interviews alone.    
• Match candidate  level to role complexity: This may sound obvious, but     ensure the candidate’s experience aligns with the seniority of the role. If you’re hiring a senior data engineer to lead an AI data initiative, you’ll want evidence of past leadership – such as mentoring junior engineers, owning the architecture of a major project, or driving best practices in a team.

During interviews, seniors should impress you with not just technical answers, but strategic thinking (e.g., trade-offs they considered, how they aligned data work with business needs).

On the other hand, if you’re hiring a junior, you might prioritize potential over polish. Maybe they haven’t worked at scale yet, but they exhibit great learning aptitude and basic skills. It’s fine if a junior’s answers aren’t perfect as long as you sense coachability and foundational knowledge. Be clear in your hiring panel discussions about what level the candidate is suitable for – sometimes you might find a great person who is a better fit fora slightly different level than initially thought. It’s not uncommon to adjust role seniority or have flexibility if a candidate shows promise (e.g., hiring a strong mid-level when you originally opened a junior role).

• Leverage assessments and recruiter expertise: If you’re not a technical expert (say you’re an HR manager), partner closely with a provider such as In4Matic.   
     

By following these practices – looking for evidence of skills, testing those skills, and evaluating how candidates handle real scenarios – you’ll increase your chances of hiring a data engineer who not only looks good on paper but can trulydeliver in the role. It’s about finding that balance of technical aptitude, practical experience, and cultural fit that will support your AI initiative’s success.

Conclusion

Hiring data engineers for AI initiatives is a strategic investment that can make or break your organization’s data ambitions. To recap the key points for HR and IT managers:

• Recognize the role’s importance: Data engineers are critical     enablers of AI – they ensure your data is usable, reliable, and delivered at the right time for machine learning and analytics. Treat these roles as     foundational to your AI strategy, not secondary support. The market demand     reflects this, with data engineering roles seeing explosive growth​
     
• Hire across levels thoughtfully: Build a balanced team with junior, mid-level, and senior data     engineers, each bringing different strengths. Juniors offer potential and support, seniors bring direction and expertise. Align your expectations     (and job descriptions) to each level’s typical responsibilities – from     building simple pipelines to architecting entire data platforms   
     
• Prioritize key technical skills: Look for candidates proficient in data pipeline architecture, cloud     platforms (AWS/Azure/GCP), SQL and Python, big data frameworks (Spark/Hadoop), and real-time data processing. These are must-haves for     modern AI data infrastructure. Bonus points for experience with MLOps,     streaming tech like Kafka, and solid data warehouse knowledge​
     
• Don’t neglect soft  skills: Seek out problem-solvers who are communicative team players. Data     engineers need to collaborate with data scientists and business stakeholders, adapt to changing needs, and continually learn new tools.     Traits like communication, collaboration, and adaptability are essential     – top candidates will demonstrate these in their past work​
     
• Stay abreast of trends: Be aware that the hiring landscape is competitive. There’s a talent     shortage, so act fast and put your best foot forward in recruiting. Emphasize what makes your role exciting (e.g. working with cutting-edge     AI). Also consider upskilling existing staff and diversifying your search     (remote hiring, etc.) to meet your needs​. The field is evolving with niche specializations – understand if you need a generalist or someone with specific domain expertise.
        
• Use a rigorous, candidate-friendly hiring process: Evaluate candidates with a mix     of resume screening, technical exercises, and behavioral interviews, things In4Matic can help with. Verify their project experience and test     their skills in realistic scenarios. At the same time, provide a good     candidate experience and move efficiently – delays or poor communication     can cost you a great hire in this hot market​
     

In a world where “data is the new oil” fueling AI, hiring the right data engineers is like building a robust refinery for that oil. By focusing on the skills and qualities outlined above, and by understanding the current market dynamics, you can identify candidates who will not only fit the job, but excel at it. These data engineers will form the backbone of your AI initiatives, ensuring that your data is flowing, your models are learning from the best information, and your business is gaining actionable insights.

With a structured approach to hiring and a keen eye for both technical prowess and soft skills, HR and IT managers can build a top-notch data engineering team. That team, in turn, will empower your organization to fully leverage AI and stay ahead in the data-driven future. Here’s to finding the data engineering talent that will take your AI projects to the next level!

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