Lukas Streit on the Skills Shift in Data Roles: Why Soft Skills are Taking Centre Stage
In a recent episode of the Objective Hiring podcast, Tim Freestone, founder of Alooba, sat down with Lukas Streit, Head of Data & Analytics at DeepImmo, to unpack how automation and AI are reshaping the future of data roles—and what that means for hiring.
“A lot of the manual or boilerplate work that data specialists do will get automated… what I still think is super important is putting the data into the actual business context.”
Lukas believes we’re on the cusp of a shift in what it means to be a “data person.” As AI continues to handle more of the repetitive technical tasks, the human value-add in these roles is moving towards problem framing, stakeholder alignment, and contextualising insights.
Soft Skills Are Becoming Essential
“Even today, it's already these data specialists that have the necessary soft skills and the necessary overview… that create the most value.”
As Lukas points out, the most impactful data professionals aren’t just strong with SQL or dashboards—they understand what matters to the business. They know how to ask the right questions, communicate findings clearly, and push back on poor assumptions.
It’s not that technical skills are going away—but the expectation is shifting. You still need to be able to challenge AI-generated output, but the competitive edge is now in what Lukas calls “soft skills with a data lens.”
Automation Isn’t the End, But a Filter
“You probably have to fight both the inner laziness… and the sense of security that AI gives you when it produces a solution that looks smooth and superficially correct.”
AI might make life easier for data workers, but Lukas warns about over-reliance. These tools can produce output that looks right but falls apart under scrutiny. Without the expertise to sense-check what AI delivers, teams risk making flawed decisions.
This is where business context and experience become irreplaceable. As Lukas puts it, seasoned analysts develop a gut feel for what will go wrong in a dataset or what checks to apply before accepting AI-generated code.
A New Kind of Data Professional?
“The hurdle to actually working with data lowers… we’ll see more specialists from other fields moving into data.”
With more automation, Lukas sees a future where domain experts—finance, customer support, marketing—use AI to become part-time data analysts. They won’t need deep technical expertise, but they will need to know how to interpret and contextualise outputs. Hiring managers should anticipate more of these hybrid profiles in their pipelines.
This trend makes it even more important to assess skills accurately and objectively—not just by looking at past job titles or buzzwords on a CV. That’s why companies use Alooba to evaluate both technical and soft skills in a structured way. If you're looking to hire data people based on ability—not background—sign up here.
Avoiding the Confidence Trap of AI
“You instinctively rate an answer better if it comes with a confident voice… even if it’s wrong.”
AI’s assertiveness can be misleading. Lukas highlights how this can erode trust in human judgment and lead to mistakes. Ironically, the most valuable human traits in data teams might be humility and doubt—knowing when not to trust the first answer, whether it’s from a person or a machine.
How to Hire for the New Skill Mix
So what does all this mean for hiring? Lukas believes the mix of skills we screen for must evolve. Here’s what matters more now:
- Business acumen and the ability to understand context
- Communication and stakeholder alignment
- The instinct to validate, cross-check, and challenge automation
- Resilience to ambiguity and soft judgment
“People who don’t understand the business context… may have the best technical skills, but their work can still fall flat.”
Recruiters and hiring managers should adapt their assessments accordingly. Traditional CV screening may overlook candidates with the right mindset but unconventional backgrounds. Tools like Alooba help companies identify high-potential candidates based on real capability—not just pedigree.
Conclusion
AI is transforming data work, but it’s not replacing people—it’s changing what makes them valuable. As Lukas Streit argues, the future of data roles will reward those who bring clarity, judgment, and communication to complex problems. Hiring managers who adapt their criteria now will build teams ready for that future.
Bridging the Gap: What This Means for Hiring Managers
If you’re responsible for hiring data professionals, these changes mean you can’t rely solely on traditional indicators like degree titles, brand-name employers, or coding bootcamp certificates. Instead, the real differentiator will be someone’s ability to interpret complexity, communicate clearly, and align with the business.
Yet, this is notoriously difficult to assess through resumes or even unstructured interviews. That’s why forward-thinking teams are turning to structured assessments to validate a candidate’s full skillset.
At Alooba, we often see companies uncover hidden talent—candidates who may have been overlooked due to non-traditional backgrounds but who perform exceptionally well on both technical and communication-based assessments. When you measure what matters, you make better hiring decisions.
How to Prepare for the New Data Role Landscape
To stay competitive, here are a few recommendations:
- Update your job descriptions to reflect the importance of communication, stakeholder alignment, and business context—not just tooling.
- Use skills assessments early in the funnel to avoid over-reliance on embellished CVs or irrelevant credentials.
- Provide real-world data scenarios in interviews or assessments to test how candidates approach ambiguity, explain their reasoning, and prioritise insights.
- Train your hiring team to recognise soft skills in technical candidates and avoid over-indexing on 'hard' skills only.
“You can have the best technical skillset… but if you don’t understand the business goal, your work can still fall flat.”
The days of hiring purely based on tech stacks are fading. Hiring for the future means hiring for adaptability, critical thinking, and domain relevance.
Final Thought
As Lukas highlighted, the most valuable data professionals of the future will be part business analyst, part translator, and part skeptic—someone who knows when to question the machine, when to dig deeper, and when to reframe the problem.
AI may write the code, but it’s still up to us to ask the right questions. Hiring people who can do that—and do it well—is where the real leverage lies.