GraphQL Developers

The saving is typically 50 to 75% against a comparable British hire once salary, employer national insurance, pension, recruitment fees and overheads are totalled. UK-based GraphQL engineers are something we place. What tends to decide it is the quality-to-cost ratio, which usually favours Eastern Europe, the Philippines or Latin America, and Eastern Europe works well for British teams at about an hour from London. GraphQL work in Britain tends to be done by senior backend engineers with API design experience, and that seniority is what you pay for locally. A two-week money-back guarantee and free replacement cover the match.

GraphQL is a query language for APIs that lets clients ask for exactly the data they need in one request, instead of stitching together fixed REST endpoints. Teams adopt it when many clients with different needs, web, mobile, partners, sit on the same backend, since it removes the endless negotiation over what each endpoint returns. A typed schema becomes a contract that tooling and AI coding assistants can read and generate against. Caching gets harder than REST, and the resolver layer can hide brutal database access patterns. Adopt it for genuine client diversity, not because it is modern.

Schema design judgement is the top skill, because a GraphQL schema is a public contract, and a bad one propagates into every client that consumes it. Under it sits performance literacy, especially the N+1 problem, batching with tools like DataLoader, and knowing what each resolver really costs the database. Security thinking is specific too, query depth limits and field-level authorisation, since GraphQL exposes a larger surface than fixed endpoints. AI assistants generate resolvers and schema types fluently, so the differentiator is taste, whether the schema models your domain or mirrors your database tables.

Give a GraphQL candidate a flawed schema and ask them to critique it, because taste in schema design is the skill you are actually buying. Salt it with the classics, database tables leaked as types, missing pagination, a mutation returning nothing useful. Follow with a performance conversation, how they would find and fix an N+1 and protect the API from a careless query. Ask when they would advise against GraphQL. Let them use an AI assistant, then ask which suggestions they rejected and why, since schema work is where generated output is plausible but wrong.

GraphQL demand is solid inside product companies with multiple clients and mature engineering teams, though it never displaced REST and no longer pretends to. It shows up steadily in backend and full-stack job specifications, usually as one expected competence among several. Federation keeps it relevant at larger organisations, acting as a composition layer over many services. It has found a modest new role in the AI wave too, a typed schema is machine-readable, making GraphQL APIs pleasant for agents. Worth building on when client diversity is a live problem, otherwise REST serves just as well.

Yes, our developers use AI assistants for GraphQL work, and the schema-first nature of the technology is actually a help in keeping the output sound. A typed schema gives generated code something to check against, and our engineers design the contract before any assistant fills in resolvers. Soundness rests on habits we vet for, reviewing generated data access for hidden N+1s and testing authorisation at field level, since those are the failures assistants introduce most readily. Every candidate is assessed on whether they use AI tooling well. We apply the same rule ourselves, a human decides.

Vetting is CTO-led and four stages long, and for GraphQL developers it is tuned to expose whether schema thinking is real or borrowed. Candidates take a technical assessment, then pair program live with a senior engineer who brings API design problems and watches how they reason about contracts and cost. A cultural fit screen and our own psychometric assessment complete the process. We use AI across screening and matching data, but the hiring decision stays human. Matched candidates interview within 7 working days and are embedded in two weeks, and retention runs at 97% beyond two years.

You are typically interviewing matched candidates within 7 working days of a requirements call, and developers are often embedded and pushing code within about two weeks. From there it is a rolling monthly contract with 30 days' notice, one monthly fee, and no placement charges. You can scale up or down as the work changes, and you pay nothing to interview.

There is a two-week money-back guarantee, and if you want to continue we replace the developer free of charge. In practice this rarely comes up, because you interview a shortlist of two candidates who have already passed a CTO-led technical assessment and a live pair programming session, so the fit question is largely settled before anyone starts.