Volume is the problem, not scarcity. Software engineering has the largest applicant pool of any technical discipline and by some distance the worst signal-to-noise ratio. A mid-level backend posting in a major European city can attract several hundred applications, most of them unsuitable. What keeps the role open is the screening cost, not the search cost, and that is a cost most internal teams cannot absorb alongside their actual jobs.

The second issue is language and location requirements. Many European employers still require working German, Dutch or French for engineering roles that do not genuinely need it, and remote-work policies vary widely across the same client base. Each constraint can halve a viable pool overnight. We will tell you exactly what each requirement costs you in candidates and in weeks on the market.

The third is seniority calibration. A great deal of what is advertised as senior is a mid-level role with a senior title and a mid-level salary. Candidates read specifications carefully and are unforgiving about the mismatch. Our shortlists include a written seniority assessment alongside the technical fit.

Cloud roles are defined by combinations, not skills. A client rarely needs “an AWS engineer”; they need someone who has run Kubernetes in production on AWS, with Terraform, in a regulated environment, at their scale. Each qualifier removes most of the market. Written specifications almost never distinguish the genuine requirements from the wishful ones, so the first thing we do is establish which two or three actually matter.

Supply is also being consumed faster than it is created. Every enterprise migration and every AI infrastructure program competes for the same senior platform engineers, and the people who have done it before are largely employed and not actively looking. We approach them directly rather than waiting for them to apply.

This is also the discipline where counter-offers are most aggressive. Losing a platform engineer is operationally painful, so employers fight hard to keep them. A three-round interview process spread over two weeks will lose these candidates. We help clients compress their process without lowering the bar.

There is effectively no unemployment in security. ISC2 puts the global workforce gap at 4.8 million people, up 19% year on year; every candidate we approach is already employed, and most are approached weekly. Traditional job advertising has largely stopped working for this discipline.

Regulation is now the demand driver. NIS2 and DORA have moved security hiring from a discretionary line to a compliance obligation for a large population of European organizations simultaneously, which means a great many companies are competing for the same small candidate pool against the same deadlines. Timing matters more here than in almost any other area we work in.

Screening is genuinely difficult. Certifications are abundant and correlate poorly with capability, and a significant share of applicants present adjacent IT experience as security experience. Our security screening is done by a recruiter working exclusively in this area, not by a generalist reading a certification list.

Job titles do not mean what they say. “Data scientist” covers everything from dashboard analytics to production machine-learning systems, and the gap between those two people is enormous. Most failed data hires are a definition failure rather than a sourcing failure, which is exactly the problem our project blueprint exists to solve.

The infrastructure layer is the real bottleneck. Most organizations that believe they need a data scientist need a senior data engineer first. Modeling talent is easier to hire than the platform talent that makes modeling useful. We will say so before you brief the wrong role.

The modern data stack is a moving target. Snowflake, Databricks, dbt and Iceberg have redrawn the map in three years, and candidates who know the current tooling on production workloads are a small population. We track vendor experience explicitly rather than treating "data engineer" as a single search.

Demand has moved faster than supply can answer. AI and machine-learning postings grew by more than 160% year on year in North America and European demand has followed the same curve. People with genuine production machine-learning experience, as distinct from notebook or hackathon experience, remain a small population, and the generative-AI subset is smaller still.

The market has three distinct populations that clients routinely conflate. Applied research engineers who train and fine-tune models. ML engineers who ship those models into production. AI product engineers who build applications on top of foundation-model APIs. All three are advertised as "AI engineers" and the wrong hire in the wrong slot delivers nothing. Our intake conversation always establishes which of the three you actually need.

Compensation benchmarks are unstable. Frontier-lab and Big-Tech comp packages have distorted the market for senior candidates, and salary data older than six months is misleading. We refresh our AI compensation data quarterly and share the ranges before you post the role.

Executive technology hires are the most consequential and the least reversible decisions our clients make. A wrong VP of Engineering costs a company a year of momentum and often a wave of attrition below them. That risk is why every leadership search we run is retained and exclusive: a longer intake with the board or hiring stakeholders, a written search brief you sign off before we begin, and systematic market mapping instead of reactive sourcing.

The market map is the deliverable that separates a serious search from a paperwork exercise. Alongside the shortlist we deliver a written map of the twenty-to-thirty people in the market who could credibly do the role, why we approached the ones we approached, and why the others were ruled out. Clients tell us it is the most useful artefact they receive from any recruitment engagement.

We also support onboarding. A first ninety days that goes wrong is usually a set-up problem rather than a candidate problem, and a short structured program in the first three months significantly reduces the rate at which executive hires do not stick.