Rethinking Software Engineering Interview Processes and Practices

Software engineers should be interviewed with practical, job-relevant tasks instead of outdated algorithm questions.

### Rethinking Coding Interviews: Bridging the Gap Between Theory and Practice
* 💻 The purpose of algorithm-style coding interviews is to **filter candidates**, but this method may not accurately reflect the **skills needed for real-world software engineering jobs**.
* 🧑 Companies like FAANG can afford to be selective due to the **high volume of qualified applicants**, leading to the absurdity of companies like Home Depot asking for **complex algorithms for simple tasks**.
* ⏳ Candidates often spend weeks or months preparing for these interviews, focusing on **algorithm memorization** rather than the **practical skills** they've developed over years of work.
* 📄 In interviews, candidates are required to code in environments like Google Docs, lacking the **supportive features of an IDE**, adding **unnecessary pressure** that doesn't exist in real job scenarios.
* 🛠 On the job, engineers typically rely on **libraries and references**, contrasting with interview expectations that they **recall algorithms from memory** without any assistance.
* 🔍 The inability to use Google during interviews is particularly ironic, as **effective searching** is a **crucial skill** for software engineers in their daily tasks.
* 📈 To improve the interview process, companies should focus on **domain-specific questions** that mirror actual job responsibilities, allowing candidates to **demonstrate their problem-solving processes**.
* 🤝 Ultimately, hiring should prioritize **practical debugging skills** over theoretical knowledge, valuing candidates who can **effectively troubleshoot and find solutions in real-time**.

Coding Interviews are Broken

I'm a software developer who makes videos about React, React Native, GraphQL, Typescript, Node.js, PostgreSQL, Python, and all things coding.

Ben Awad

Critique of Algorithm-Based Coding Interviews

Algorithm-style interviews are designed to be difficult, filtering candidates for FAANG companies.

The problem arises when other tech companies copy this model, using complex algorithms for less demanding roles.

Job tasks differ significantly from interview questions, requiring extensive preparation.

This is especially problematic for intermediate/senior engineers who must study for weeks despite years of experience.

Comparison of Google Interviews vs. On-the-Job Tasks

Coding in a Google Doc versus using an IDE with features like syntax highlighting and autocomplete.

High-pressure interview environment compared to the more relaxed atmosphere of on-the-job tasks.

Algorithm focus in interviews versus infrequent algorithm use in daily work.

Prohibition of using Google during Google interviews, despite the importance of online research skills.

Qualified candidates fail due to the disconnect between interview and job tasks.

The solution is to align the interview process with the actual job requirements.

Example: using a real-world bug, providing necessary tools, and allowing Google searches.

Advocating for domain-specific questions focused on actual job duties.

Prioritizing practical debugging skills over rote memorization of algorithms.