Insights
Published methodology and market notes.
Written for the people who own hiring outcomes: what the research shows, how the method works, and where the assumptions behind a hiring plan tend to break.
Published
Start here.
Market analysis
Why commissioning talent is the hardest hire in data center delivery
The size of the real commissioning bench, why Level 4 and 5 experience cannot be trained on your schedule, and how to sequence the hire.
Methodology
How to model the cost of a vacant data center role
The four inputs that make a vacancy number defensible, why schedule risk dominates in infrastructure, and how to compare vacancy exposure with search investment.
Playbook
Hiring sequence for data center roles
Working-backward planning by role family, the four places searches lose momentum, and how to plan hiring against a turnover date.
Playbook
Hiring critical facilities teams that stay
Shift structure, incident-judgment screening, and the retention levers that matter more than a pay bump when staffing a 24/7 site.
Role analysis
Why a GPU cluster engineer and an AI engineer are different hiring markets
Titles overlap, pipelines mix, and searches stall. The distinction that fixes the scorecard, and the comp and sourcing differences that follow from it.
Role analysis
Commissioning hiring: CxA, QCxP, controls engineer and startup technician
What each commissioning-side role actually owns, how to screen it, and the sequencing mistake that puts turnover at risk.
Market analysis
Controls and BMS hiring: the software layer inside a physical facility
Platform depth, protocol fluency and checkout discipline: the screen that separates real controls engineers from keyword matches.
Hiring strategy
How to scope a Head of Infrastructure for a GPU cloud
Fleet operations, platform, networking or everything: what the seat should own at your stage, and how to write a scorecard the right candidates respect.
Hiring strategy
What an aging critical facilities requisition usually signals
The four real causes behind an aging facilities req, how to diagnose which one you have, and what to change before more sourcing spend.
Market analysis
Data center hiring in Phoenix: power, cooling, semiconductor competition and talent supply
What the Phoenix talent market actually looks like from live searches: the pools that are deep, the ones that are not, and how to sequence a build.
Hiring strategy
AI infrastructure team design: network, platform, SRE, MLOps and capacity planning
A buyer's framework for the five infrastructure functions: what each owns, how to tell candidates apart, and what to hire first.
Trends
AI hiring trends reshaping data center teams
What AI buildouts changed about scope, sequence and screening, and which roles employers now compete hardest for.
Market analysis
Data center recruitment: what changed in 2026
Why job postings stopped working, which adjacent industries the bench comes from, and how to plan hiring against a construction schedule.
Methodology
How AI supports screening without making the decision
The evidence, governance and human-accountability framework behind AI-assisted sourcing and screening in this practice.
Playbook
Sequencing a site launch from early planning to turnover
Which benches must be in motion at each milestone, and the workforce risks worth escalating before they affect launch.
Playbook
What a talent market map actually contains
Named universes, supply density by metro, observed compensation ranges and adjacent pools, and how the research changes hiring plans.
Reference
Engagement scopes for hiring leaders
Homepage scopes for talent intelligence, embedded search, team builds and leadership search.
Working notes
Patterns that keep repeating.
Observations from live searches and market research, stated plainly. Where a claim is directional rather than measured, it says so.
Supply is regional, not generic
Critical facilities and commissioning talent clusters around existing campuses. A broad average tells you nothing about whether a specific metro can staff a specific site.
Scope, not sourcing, blocks most searches
Roles that stall usually have a constraint in the job description: travel, shift, certification, or a comp band written before the last build cycle.
AI infrastructure hiring competes outside your industry
Platform, SRE and MLOps candidates benchmark against product companies. Infrastructure employers win on scale of problem and clarity of mission, not on posting volume.
Interview velocity is a hiring lever
Feedback latency is the single most controllable variable in a competitive market. A 48-hour loop converts candidates that a two-week loop loses.