Location Matters Part 2: Matching Engineering Location to Task Ambiguity

Editor’s Note: This article is Part 2 of a 3-part blog series exploring the strategic configuration of global engineering talent across onshore, nearshore, and offshore talent pools.

When evaluating onshore, nearshore, and offshore engineering options, companies frequently focus on hourly rates or geographic proximity. However, geography alone does not tell you whether an engineer can independently turn an ambiguous operating problem into working software. The real variable is how much ambiguity the work contains.

The Real Variable Is How Much Ambiguity the Work Contains

Consider two engineering assignments.

The first is: “Implement these defined tickets against this existing architecture.” The requirements have been established, technical decisions have been made, dependencies are understood, and the work can be reviewed against clear acceptance criteria.

The second is: “Sit with our COO, determine why this workflow is broken, design a better system, and iterate with the people who use it.”

Both require technical competence, but they are not the same kind of work. The first primarily requires implementation capacity. The second requires investigation, product judgment, stakeholder management, architectural decision-making, and the ability to translate business pain into a technical system.

The difference is not the level of technical difficulty. A highly complex optimization problem can still be tightly defined. A seemingly simple internal workflow can be extraordinarily ambiguous because no one agrees on how the current process works, what the actual constraint is, or what a successful replacement should accomplish.

As work becomes more defined, the viable global talent pool expands. Nearshore and offshore engineers can execute clearly bounded work extremely effectively because the company has already resolved the most consequential uncertainties. As ambiguity increases, communication bandwidth, organizational access, and independent judgment become more important.

A senior US-based engineer may be the right choice for a US company when that person must work directly with executives, observe how users behave, challenge assumptions, and make decisions without waiting for a complete specification. The premium in that situation is not necessarily a coding premium. It is an ambiguity-reduction premium.

What This Looks Like in Real Projects

The distinction becomes clearer when applied to actual engineering work.

In one Gun.io engagement with a manufacturing client, the client’s apparent need was software capacity. The underlying problem was a collection of finance and operational processes dependent on manual spreadsheets, disconnected data, and recurring intervention from senior personnel.

There was no clean backlog that captured the real work. The engineer first had to understand how payroll, commissions, inventory, purchasing, and month-end reporting operated across the business. That discovery led to direct extraction and transformation of ERP data, automated payroll and commission workflows, work-in-progress and finished-goods visibility, self-hosted integration infrastructure, and secure internal access to the resulting applications.

This was not simply a case of implementing predefined tickets. The engineer had to sit with the people doing the work, determine where the process was breaking down, identify missing source data in the legacy commission process, and design a system that the finance and operations teams could actually validate and adopt.

Once the data foundation and workflows were defined, more of the implementation became divisible. Individual reporting outputs, integrations, reconciliation steps, and application features could be assigned across a broader team. The early work required concentrated context and judgment; the later work could take advantage of distributed execution.

A second engagement involved a healthcare-data platform containing approximately 750,000 lines of SQL across more than 60 databases. The original work centered on audit remediation, technical continuity, and support for a cloud migration. As the engagement progressed, the engineer encountered urgent customer fixes, DevOps troubleshooting, calculation corrections, product stabilization, and broader technical triage.

A ticket that appeared discrete could not be treated as isolated because the platform was highly coupled, under-documented, and difficult to observe. Before estimating or changing the system safely, the engineer had to trace dependencies, understand transformation logic, determine downstream effects, and distinguish whether a problem came from infrastructure, application behavior, or data quality.

The delivery challenge was not a shortage of coding tasks. It was that discovery, remediation, customer support, and migration work were competing for the same engineering capacity without a sufficiently explicit priority model. Adding more engineers without resolving that ambiguity would have increased the coordination surface area without necessarily increasing the amount of risk retired.

A third engagement involved three senior remote engineers supporting an aerospace company’s product, platform, simulation, and backlog needs. The engineers were capable, but early productivity was materially affected by equipment, training, repository access, VPN access, and general onboarding readiness. The average engineer took 17 days from their start date to become fully productive.

After onboarding, the larger constraint was integration into the client’s operating rhythm. One engineer participated in the daily core standup while two others did not. One developer self-sourced backlog tickets and checked whether they were still relevant. Another was redirected from platform work to user-management and database work while remaining the expected owner when their original project restarted. A small ticket assigned to a third engineer expanded into a larger feature and then paused while the team resolved UX and production-use questions.

None of these issues were principally geographical. They were questions of access, context, assignment, and priority ownership. The engineers did not need to be physically closer to the company. They needed sufficiently current work, direct access to the relevant operating forums, and clarity about who could change their priorities.

These engagements point to the same conclusion: distance matters most when the operating model forces distance to carry unresolved ambiguity.

Conclusion

Categorizing work by ambiguity rather than geography allows engineering leaders to deploy talent strategically. When you concentrate coordination where uncertainty is high and expand the talent market where the work has clear boundaries, location becomes an advantage rather than a constraint.

Further Reading in This Series:

• Part 1: Redefining Onshore, Nearshore, and Offshore Engineering Talent — Explore what these terms actually mean and debunk common myths about talent location.
• Part 3: The True Cost of Distributed Teams & Building the Mixed Model — Learn why cheap implementation can become expensive due to coordination costs and how to build a hybrid global team.