The Cost of Subpar Development: Why Hiring Mistakes Can Be More Expensive Than You Think
When managing software projects, one of the most overlooked yet critical decisions is the choice of developer. It’s often assumed that fixing bugs or adding features is just part of the process, but the reality is that investing in poor-quality code can end up costing more in the long run than you might imagine. Here’s a look at two real-world examples that highlight why hiring or retaining a bad developer can be far more costly than replacing them early on.
Case Study 1: The Symptom of Poor Coding in a Weather API Project
In a small-scale project built with Symfony, a developer was tasked with creating an API endpoint that returns the latest weather data from various stations. The endpoint was designed to fetch either all stations or a specific one, based on the provided ID. Unfortunately, the implementation was far from optimal.
Instead of retrieving only the most recent data for each station—a simple and efficient query—the developer pulled all historical entries (about 30,000 per station at the time) and sent the entire dataset to the frontend. When accessed, this resulted in sluggish performance: loading times exceeded six seconds due to sequential requests that compounded response delays, especially when several stations responded slowly.
Furthermore, the API sent enormous amounts of data (around 80MB) unnecessarily. Much of this was due to retrieving and transmitting duplicate or irrelevant information, which bloated the response size. This inefficient approach meant that three developers at the company had to spend significant time fixing what was essentially a flawed foundation laid by a single developer.
Case Study 2: Data Mishandling and Performance Issues in an E-Commerce Application
In another project, a freelance developer faced recurring performance bottlenecks and data consistency problems. One particular issue was related to delivering incorrect inventory data to clients, where items listed on delivery notes did not match orders because the code retrieved separate lists of items without maintaining consistent orderings. This mismatch was caused by fetching two separate datasets from the database without ensuring proper synchronization.
Additionally, legacy code relied heavily on static arrays of IDs instead of dynamic database relationships, which hampered scalability and introduce errors. These inefficiencies, combined with poorly optimized queries like N+1 problems, led to performance degradation and delays in deploying new features—sometimes doubling development time unnecessarily.
The Takeaway: Prioritize Code Quality or Pay the Price
These examples underscore a fundamental principle: investing in a competent, reliable developer upfront can save a company
