Written by —
Rauno Paukkeri shows you how to identify a skilled data engineer and avoid hiring the wrong one. Concrete tips for making the right recruitment choice.
Written by —
🇫🇮 This blog is also available in Finnish (original version); please find it using the language switcher on the top right corner.
the blog in a nutshell 🌰
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A skilled data engineer is essential for reliable data infrastructure and enabling data-driven decision-making
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The data engineer title can hide a lack of the right skills, and bad hires can be costly
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A “fake" data engineer isn’t always dishonest - often just a mismatch in experience or mindset
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Red flags include too much jargon, weak business understanding, and avoiding responsibility
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Assess candidates through practical tasks, technical discussions, and communication evaluation
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Look for answers to how and why, production experience, and reflection on past mistakes
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A great engineer is solution-oriented, sees the big picture, and communicates clearly.
The role of data engineering in organizations
Data is one of the most important enablers of modern business. But simply collecting data isn’t enough. You need to refine it into a usable, business-ready format. And that takes skilled data engineering.
A professional data engineer can transform messy, siloed data into a cohesive, high-quality, and scalable ecosystem. But what happens if they can’t?
With the rise of cloud platforms and AI solutions, the job title “data engineer” has become increasingly common. Yet the role itself remains fuzzy for many, which means the title possibly could hide serious gaps in knowledge.
The work of a data engineer is more than just coding. It forms the foundation of the entire data infrastructure. A professional data engineer ensures that data flows seamlessly from source systems to AI, data solutions, and business reports, supporting informed decision-making. Without this link, a company can drown in data. Data may be available, but it doesn’t become value-creating information.
In this blog, we’ll break down what a “fake” data engineer looks like - someone who might hold the title but lacks the skills. And we’ll share practical tips to help you spot the real deal.
Why the data engineer role is critical for modern organizations
Skilled data engineers understand that every data point matters. They take care of data integrity, traceability, and usability, all essential for reliable analytics, AI models, and reporting.
Moreover, they have comprehensive expertise in cloud platforms such as Azure, AWS, and Google Cloud Platform. They build durable data pipelines and automated processes by applying CI/CD and DataOps principles. Data modeling, storage, and visualization are carried out using modern tools like dbt, Power BI, Snowflake, and Databricks.
When your data infrastructure is thoughtfully designed and actively maintained, your organization can make confident, informed decisions.
Why hiring the wrong candidate can be costly
If a data engineer can’t build a solid, sustainable solution, the consequences can pile up quickly. Poorly built architectures, fragile integrations, and a lack of automation lead to slow development, project delays, downtime, faulty reports, unreliable analytics, and inflated costs.
At worst, trust in the organization’s data crumbles. That’s why a “fake” data engineer can be a serious risk.
What is a “fake” data engineer?
The term “fake” doesn’t necessarily mean someone is consciously being dishonest. Often, it’s about mismatched skill sets, limited thinking, or narrow experience.
Take someone from a front-end background who knows basic SQL but lacks knowledge of cloud infrastructure, data modeling, or orchestration. Or a consultant who name-drops dbt or Databricks but has never taken a data solution to production. Or someone who doesn’t quite see why data quality matters.
These kinds of individuals typically have narrow (data) experience, an inadequate understanding of business needs, and a tendency to avoid concrete examples.
What Recordly’s data engineers bring to the table
At Recordly, our data engineers are architects, integrators, and business-minded problem solvers. They design and implement modern data architectures that move data smoothly from source to end-user. They build scalable, cloud-native solutions and make sure the data is always fresh and in the right shape. That’s what enables impactful analytics, trustworthy AI, and new business opportunities.
They’re fluent in cloud platforms. They build robust data pipelines and automated processes. They handle data modeling, storage, and visualization.
Most importantly, they understand how technology connects to business. They make thoughtful decisions, communicate clearly, collaborate seamlessly and always ask the essential question first: “Why?”.
Five red flags to watch for in a data engineer candidate 🚩
- Buzzwords with no substance
Their CV is filled with phrases like “real-time data mesh architecture using dbt on Databricks,” but they can’t explain the problem they solved or how they approached it. - No understanding of business needs
If they only talk tech, without linking it to real-world outcomes, that’s a major warning sign. - No mention of failures
If all their projects have “gone perfectly,” you’re either talking to a superhero or someone who cannot identify their mistakes and learn from them. - Knows the tools, but not the system
Familiarity with SQL or having “played around with dbt” isn’t enough. They need to understand the broader context of data production. - Avoids teamwork and hates documentation
Collaboration and documentation are essential in data projects. If they resist either, think twice.
How to assess data engineering skills in the hiring process
- Give a practical scenario
Example: “The client has an ERP system and wants to move data into an analytics platform. What would you do?”
Don’t look for the perfect answer. Focus on how they think, what choices they make, and whether they get the business angle. - Hold a conversational technical interview
Ask questions like:
- “Which project are you most proud of?”
- “What was your latest mistake, and what did you learn?”
- “How do you ensure scalability and maintainability?”
- “Which project are you most proud of?”
- Test communication and collaboration
Example: “How would you explain solution X to a non-technical stakeholder, such as a leadership team?” - Go deep on one or two topics
Instead of checking a dozen boxes, dig into one or two areas to assess real experience that would be important for the position you are offering. For example:
- “How do you use dbt in production?”
- “How would you set up a CI/CD pipeline in Databricks?”
- “How do you ensure data quality and traceability?”.
- “How do you use dbt in production?”
How to recognize a skilled data engineer
- Ask how and why, not just a list of tools they are familiar with
- Make sure they understand the full data lifecycle and why it matters for business
- Confirm they’ve taken solutions into production and owned the outcome
- Look for clear communicators who collaborate well
- Listen for reflection. Do they talk openly about successes and failures?
- Aim for a solution-oriented thinker. Technical execution alone isn't interesting.
Final thoughts
While spotting a skilled data engineer isn’t rocket science, it does take the right questions, genuine curiosity, and a bit of patience to make it right.
I hope this guide helps you make your next hire with confidence!
And if you’d like help with your own recruitment or need additional experts for your data projects, we’re here to help and happy to assist you. Get in touch with us.
