Published 8 October 2026

What Are the Key Skills to Look for When Recruiting for Tech and Data Roles?

A hiring manager posting a data engineer role in London or Cape Town often cannot tell from a CV alone whether a candidate has run production pipelines or only built a classroom project.

What technical skills does a tech or data role need?

AiR Talent Group recruits for tech, product, data and AI roles across South Africa, the UK, the Gulf and the US, and its own job specifications show what separates a strong candidate from a padded one: depth in one technical area, the ability to work with a team, and real scope of past ownership [9].

The technical skills worth screening for depend on the specific role, but a pattern repeats across AiR Talent Group's own postings. A business analyst role asks for comfort with SQL and NoSQL data analysis, experience mapping API integrations, and work inside Agile delivery environments [1]. An analytics engineer role asks for scalable data warehouse models and the ability to develop and optimize ETL and ELT processes [2]. Neither skill set substitutes for the other, so a job spec that blends them without naming which matters most for the role risks attracting the wrong candidate.

Seniority changes what good looks like. AiR Talent Group's Senior Data & Machine Learning Engineer brief asks for 5-7 years in data or machine learning engineering, because the pipelines support products used by brands across 32 markets [6]. That scale test matters more than a tools list on a CV, since a candidate who has run pipelines at that size has usually debugged failures under pressure, not followed a tutorial. A data engineer role built around UK telecom infrastructure asks for comfort processing large datasets for a similar reason [4].

A general checklist helps when a role sits outside your usual hiring pattern. Coursera's guide to in-demand IT skills groups the work into security, programming, systems and networks, data analysis, DevOps and cloud computing, and treats security as foundational because it underpins the rest of a team's output [11]. Use that list as a starting filter, then test real depth in the one or two areas the specific role needs, rather than screening every candidate against the full list.

Why do soft skills carry as much weight as technical skills?

Soft skills matter because most tech and data work happens inside a team, not alone at a terminal. CompTIA's guide on landing a tech job argues that communication, adaptability and critical thinking often separate strong candidates from the rest, alongside whatever technical skill the role demands [10]. A developer who can explain a trade-off to a non-technical stakeholder saves a team weeks of rework, which a skills checklist alone will not catch.

AiR Talent Group's own postings reflect that expectation. A senior BI engineer role is not about building dashboards; it asks the person to partner with data engineering on pipelines and quality, and to translate business requirements into data products other teams can use [5]. That kind of brief only works if the candidate can listen to what a stakeholder needs and push back when a request does not match the data available.

When you write a job spec, name the outcome that needs coordinating with other teams, not only the tools involved. A candidate who has only worked solo on a project will struggle with that part of the role, even with a strong technical test score, so build a question into your interview that tests how they have handled disagreement with a stakeholder before.

How can you tell real skill from an inflated job title?

Ask what the candidate owned, not what their title says. Job titles inflate fast across tech and data roles, so two candidates both called 'Senior Engineer' can carry very different scopes of decision-making behind the same label.

AiR Talent Group's postings work around this by naming the outcome a role owns rather than leaning on a title. The senior BI engineer brief, for example, states the person will own BI dashboard development and optimization end to end [5], which gives a hiring manager a concrete thing to verify in an interview instead of taking a past title at face value.

In an interview, ask a candidate to walk through one decision on a recent project: what the options were, what they chose, and what happened after. A candidate with real ownership gives a specific, sometimes messy answer, while a candidate coasting on title inflation gives a generic one. This single question filters harder than most technical tests, because it checks judgement under real constraints rather than a rehearsed response.

When should you bring in a specialist recruiter instead of hiring alone?

Bring in a specialist recruiter when your own pipeline is too thin to compare candidates fairly, or when screening at the volume a tech or data search needs would take weeks you don't have. AiR Talent Group pairs an AI agent, Aria, that sources and screens candidates, with consultants who make the final call [9], and the same consultant owns the client relationship from the first brief through to placement [7].

That split matters for skills assessment specifically. The AI layer narrows a large pool fast, but a person still checks whether a candidate's stated experience matches the scope a role needs, which is the step a pure keyword filter tends to miss. For a niche data or AI search, that combination can save the time a hiring manager would otherwise spend sorting CVs alone.

If you are weighing whether to run a search in-house, start by counting how many qualified candidates you can realistically reach without outside help. If that number is small, a specialist recruiter's pipeline is the faster route to a shortlist worth interviewing.

What does AiR Talent Group look for across tech, data and AI roles?

AiR Talent Group recruits for tech, product, data and AI roles across South Africa, the UK, the Gulf and the US [9], and its view of a strong candidate is shaped by watching similar roles fill across several markets rather than one. In its own writing on AI and hiring, the company points to a 25% figure often cited for existential risk from AI and argues the bigger risk for employers is failing to adapt their talent strategies fast enough [3], which puts adaptability on the same footing as technical depth when screening a candidate.

The same filter applies outside pure tech. AiR Talent Group also recruits for agriculture, logistics and supply chain employers across 19 African countries, placing both local and expatriate hires through a single consultant who handles both business development and delivery [8]. Whether the hire is a machine learning engineer or an operations lead, the same question applies: what has this person owned, and can they adapt when the role changes under them?

If you are building a tech or data team now, start the brief with the outcome the hire must own, then work backward to the tools and traits that make that possible. That order catches more of the gap between a CV and the job than a skills checklist read in isolation.

What technical skills does a tech or data role need?

Why do soft skills carry as much weight as technical skills?

How can you tell real skill from an inflated job title?

When should you bring in a specialist recruiter instead of hiring alone?

What does AiR Talent Group look for across tech, data and AI roles?

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