Your API is Over-fetching, Under-fetching, and Has Too Many Endpoints. GraphQL Was Meant to Fix That.
Traditional REST APIs have a fundamental problem: the shape of the data returned is determined by the server, not the client. This leads to a constant struggle where mobile and web clients are either "over fetching" (downloading huge JSON payloads with data they don't need) or "under fetching" (requiring multiple round trips to different endpoints to get all the data for a single screen). GraphQL solves this problem by giving the client the power to ask for exactly what it needs in a single request.
But this power comes with a new set of challenges. In the hands of a developer who doesn't understand the underlying principles of graph data modeling and resolver optimization, a GraphQL API can become a performance nightmare. A single, poorly constructed query from a client can trigger a cascade of inefficient database calls on the server, leading to the infamous "N+1 query problem" in a new and more dangerous form.
This playbook explains how Axiom Cortex vets for a deep, practical understanding of GraphQL, finding engineers who can design robust schemas, write efficient resolvers, and build scalable, production grade GraphQL APIs. Similar principles apply to other modern backend technologies like gRPC.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "GraphQL" on a résumé and assumes proficiency. The interview might involve asking the candidate to write a simple query. This superficial approach fails to test for the critical skills needed to design and operate a real world GraphQL server.
The predictable results of this flawed vetting are common:
- The N+1 Query Disaster: A client requests a list of 100 blog posts and the name of the author for each one. The server executes 1 query to get the posts, and then 100 separate queries to get each author's name, resulting in 101 database calls for a single API request.
- Schema Chaos: The GraphQL schema is poorly designed, with inconsistent naming, no clear separation of concerns, and a failure to use interfaces or unions to model the domain effectively.
- No Caching Strategy: The team has no strategy for caching GraphQL queries, leading to unnecessary load on the backend for frequently requested data.
How Axiom Cortex Evaluates GraphQL Developers
Axiom Cortex is designed to find engineers who think in terms of graphs, schemas, and efficient data fetching. We test for the skills that are essential for building high performance GraphQL APIs. We evaluate candidates across three critical dimensions.
Dimension 1: Schema Design and Architecture
The schema is the single most important part of a GraphQL API. This dimension tests a candidate's ability to model a business domain effectively.
We provide a business problem and evaluate their ability to:
- Design a Clean Schema: Can they design a clear and intuitive schema with well defined types, queries, and mutations?
- Use Interfaces and Unions: Do they know how to use interfaces and unions to model polymorphic relationships in their data?
- Design for Evolvability: Can they design a schema that can evolve over time without breaking existing clients?
Dimension 2: Resolver Optimization and Data Loading
This is the core of building a performant GraphQL server. This dimension tests a candidate's ability to solve the N+1 problem.
We present a complex query requirement and evaluate if they can:
- Implement the Dataloader Pattern: A high scoring candidate will immediately talk about using the Dataloader pattern to batch and cache database calls within a single request. Can they explain how it works?
- Write Efficient Resolvers: Can they write resolver functions that fetch data efficiently from the underlying data sources?
Dimension 3: Security, Caching, and Operations
An elite GraphQL developer understands how to operate their API securely and at scale.
We evaluate their knowledge of:
- Authentication and Authorization: How would they implement permissions in a GraphQL API? Do they know how to protect certain fields from being accessed by unauthorized users?
- Query Cost Analysis: Are they familiar with techniques for analyzing the complexity of incoming queries to prevent clients from requesting too much data and overloading the server?
- Caching: Can they explain different strategies for caching GraphQL results, both on the client and on the server?
From Chatty Endpoints to an Efficient Data Graph
When you staff your team with engineers who have passed the GraphQL Axiom Cortex assessment, you are investing in a team that can build a truly modern and efficient API layer. They will not just replace your REST endpoints one for one; they will build a flexible data graph that empowers your frontend teams to build better products, faster. For different API technologies, see our playbook for REST API design.