Plenty of asset-heavy employers are being asked about digital twins: a live, location-based model of a plant, a utility network, a campus or a fleet. The software gets most of the attention. The hiring is what usually decides whether the project works.
This is a practical look at the people a twin needs, written for operations and technology leaders who have to staff one in Canada or the US.
What you are actually building
A digital twin ties together three things: where an asset is, what it is (condition, age, owner, maintenance history), and what it is doing now (sensor and work-order data). Put those in one model and planners, engineers and maintenance crews work from the same picture.
None of that is magic. It is data work, and it fails when the data is incomplete, out of date or owned by nobody.
The roles that matter, in rough order
1. A data owner on the operations side. Before any technical hire, someone in operations needs to own asset records. This is often a maintenance planner, reliability lead or operations analyst. If nobody is accountable for keeping records current, the twin goes stale within a year.
2. A geospatial or GIS analyst. This person builds and cleans the location layer: maps, networks, parcels, facility footprints. Look for experience loading messy records from several systems and reconciling them. Tool familiarity matters less than that habit.
3. A data engineer. Sensor feeds, work-order systems and asset registers need pipelines that run without somebody babysitting them. This hire turns a one-off project into something that updates itself.
4. A 3D or reality-capture specialist, where needed. Facilities with dense or complex layouts may need scanned or modelled conditions. Many employers contract this work for the initial capture rather than hiring permanently.
5. An analytics or AI profile. Once the data is reliable, someone can build forecasting and what-if scenarios on top of it. Hiring this person first is a common and expensive mistake. They end up cleaning data for months.
Permanent or contract
The build phase and the run phase need different staffing.
- Build phase: a 6 to 12 month spike in data engineering, GIS and capture work. Contract talent fits well because the scope is clear and the peak is temporary.
- Run phase: a smaller steady team that maintains data quality and supports users. These roles should be permanent, and ideally filled by someone who sat through the build.
A workable pattern is to bring in contractors for the build, hire 1 or 2 permanent people alongside them, and plan knowledge transfer from week one. Contractors who leave with the only copy of how the data fits together leave you with a model nobody can maintain.
What to screen for
Titles in this area are inconsistent. A "GIS developer" at one employer is a "spatial data engineer" at another. Screen on what the person has done instead.
- Have they worked with asset data that came from more than one system, and how did they resolve conflicts?
- Can they explain a data quality problem they found and what changed because of it?
- Do they talk to maintenance and operations staff comfortably, or only to other technical people?
- Have they supported a model in production, not just delivered a prototype?
That last question separates people who can launch a project from people who can keep it alive.
Where the market is tight
Geospatial skills combined with data engineering are scarce in both countries, and utilities, municipalities, energy companies and engineering firms compete for the same people. Expect longer searches for candidates who have both. If you cannot find them, hire for one strength and train the other. A strong data engineer can learn spatial concepts faster than a mapping specialist can learn pipeline work, though your situation may differ.
Pay reflects the overlap. Budget for the combined profile rather than benchmarking against a general analyst role.
Start small and name the question
The projects that work usually begin with one asset class and one decision: which pumps to replace first, where to add capacity, which lines carry the most risk. A narrow starting point lets you hire 2 or 3 right people instead of a team of 10 for a vague mandate.
What to do next
List the single decision you want the twin to improve, then write down who owns the data behind it today. If that answer is unclear, fix it before posting a technical role. Then decide which parts of the work are a peak and which are permanent.
If you are staffing a digital twin or asset data project, a Traverse recruiter can help you scope the roles and find people for both the build and the run.