Y'all, I've been on both sides of technical interviews more times than I can count. After running Protocoding for over a year and sitting through probably 200+ technical interviews, I can tell you that most companies are doing this completely wrong. They're asking the wrong questions, testing the wrong skills, and missing out on great engineers while hiring people who can't actually build software. The whole industry has this backwards approach where we test computer science trivia instead of the skills that actually matter when you're shipping code to production.
Here's the thing that drives me crazy. I've seen engineers ace every leetcode question you throw at them, but then they can't debug a simple API integration or figure out why their React component isn't re-rendering. Meanwhile, the person who struggled with the binary tree problem might be the one who actually ships features on time and writes code that other people can understand. We've created this weird hiring culture that rewards memorization over problem-solving, and it's costing us good engineers.
We Care About Communication More Than Algorithms
The biggest predictor of whether someone's going to succeed on our team isn't their ability to implement quicksort from memory. It's whether they can explain their thinking clearly while they work through a problem. I remember interviewing this one engineer who couldn't solve the coding challenge we gave them, but they talked through their approach so clearly that we could see exactly how they think. They explained what they were trying, why it wasn't working, and what they'd try next. We hired them on the spot, and they turned out to be one of our best engineers.
Communication becomes even more critical when you're working with clients. At Protocoding, our engineers aren't just writing code in isolation. They're explaining technical concepts to non-technical stakeholders, debugging issues with client teams, and sometimes jumping on calls to walk through solutions. The engineer who can clearly explain why we chose React over Vue, or why their database design will scale better, is worth their weight in gold. These soft skills separate good engineers from great ones.
We test this by having candidates walk us through their code as they write it. Not in a formal presentation way, but just naturally explaining their thought process. The best candidates don't just say what they're doing, they explain why they're doing it that way. They'll say something like, 'I'm using a hash map here because we need O(1) lookup time, and I know we're going to be calling this function frequently.' That shows they understand both the implementation and the business impact.
Show Us You Can Actually Build Things
Portfolio projects tell me more about a candidate than any coding challenge ever will. I want to see that you've built something from scratch, deployed it, and dealt with real-world problems. It doesn't have to be the next Facebook, but it needs to be something you actually finished and put out in the world. I've hired engineers based on a simple todo app that had really clean code and good error handling over engineers with fancy AI projects that were half-finished and barely functional.
The projects that impress me most are the ones that solve real problems. I remember one candidate who built a tool to help his local restaurant manage orders during COVID. It wasn't technically complex, but he identified a problem, built a solution, got real users, and iterated based on feedback. That's the kind of thinking we need. Compare that to the candidate who built a blockchain-powered social media app that nobody used and had no clear value proposition. Technical complexity without purpose doesn't impress me.
When reviewing portfolios, I'm looking for a few key things. Can you write clean, readable code? Do you handle edge cases? Have you thought about user experience? Did you deploy this somewhere I can actually try it? Most importantly, can you explain the technical decisions you made and why you made them? These projects show me you can take an idea from concept to completion, which is exactly what we need at a consulting company.
Problem-Solving Beats Pattern Memorization
Instead of asking you to implement a red-black tree, I'm going to give you a messy piece of code and ask you to debug it. Or I'll describe a vague client requirement and see how you break it down into actionable tasks. These scenarios mirror what you'll actually be doing on the job. Real software engineering is mostly about debugging existing code, integrating with third-party APIs, and figuring out why something works in development but breaks in production.
- Debug a React component that's causing performance issues
- Design a database schema for a client's specific use case
- Walk through how you'd approach integrating with a poorly documented API
- Explain how you'd handle a production bug that's affecting 10% of users
- Break down a vague feature request into specific, actionable tasks
The best candidates approach these problems systematically. They ask clarifying questions, make reasonable assumptions, and think about edge cases. They don't just jump straight into coding. They spend time understanding the problem first. When debugging, they form hypotheses about what might be wrong and test them methodically. This is the kind of thinking that translates directly to client work, where requirements are always changing and nothing ever works exactly as documented.
I had one candidate who spent 15 minutes just asking questions about our debugging scenario before writing any code. Some interviewers might see this as stalling, but I saw it as exactly the right approach. By the time they started coding, they had a clear understanding of the problem and a plan to solve it. They found the bug faster than candidates who just started randomly changing things. That's the difference between engineering and just coding.
We Look for Learning Ability Over Current Knowledge
Technology changes fast, especially in our space where we're building AI-powered applications and working with cutting-edge tools. The React expert who can't learn Vue when a project needs it isn't as valuable as someone who's adaptable and curious. I'd rather hire someone with solid fundamentals who's excited to learn new things than someone who's specialized in exactly what we're using today. In six months, we might be working with completely different tools.
We test this by asking about times you've had to learn something new quickly. Maybe you joined a team using a technology you'd never worked with, or a client requested a feature that required picking up a new framework. The best candidates have specific stories about diving into documentation, building small test projects to understand concepts, and asking smart questions when they got stuck. They can walk you through their learning process and show that they know how to get unstuck when facing unfamiliar problems.
One thing that really impresses me is when candidates can explain something they learned recently in simple terms. If you can break down a complex concept so that a non-technical person could understand it, that shows you really get it. We had one candidate explain GraphQL to us like they were talking to their grandmother. They covered the core concepts, why you'd use it over REST, and what the tradeoffs are, all without using unnecessary jargon. That's the kind of understanding that comes from actually learning, not just memorizing.
Cultural Fit Matters More Than You Think
At a small consulting company like Protocoding, every hire matters. We can't afford to have someone who's technically brilliant but impossible to work with. I've made this mistake before, hiring someone with incredible skills who turned out to be condescending to clients and difficult to collaborate with. They lasted three months. Now I pay just as much attention to how candidates interact with our team as I do to their technical abilities.
The consulting environment is unique because you're constantly context-switching between different clients and projects. One week you might be building a healthcare dashboard, the next week you're working on a fintech API. This requires a certain kind of personality. You need to be comfortable with ambiguity, good at picking up new domains quickly, and able to work independently without a lot of hand-holding. Some engineers thrive in this environment, others hate it.
We test cultural fit by having candidates meet with multiple team members, not just the technical lead. Our project managers and designers have great insights into whether someone will work well with clients. We also pay attention to how candidates handle feedback during the technical interview. Do they get defensive when you point out a bug in their code, or do they say 'good catch' and fix it? Small reactions like this tell you a lot about how someone will handle code reviews and client feedback.
“The best engineers I've hired weren't necessarily the smartest people in the room, but they were the ones you wanted to be in the room with.”
What This Means for Candidates
If you're preparing for interviews, spend less time grinding leetcode and more time building things. Have a portfolio ready that shows you can take projects from start to finish. Practice explaining your code and your technical decisions out loud. Work on your communication skills just as much as your technical skills. And most importantly, be curious and honest about what you know and don't know. We'd rather work with someone who says 'I haven't used that technology, but here's how I'd approach learning it' than someone who pretends to know everything.
For companies doing technical interviews, stop asking questions that test memorization and start testing the skills you actually need. Give candidates real problems to solve, let them use Google and Stack Overflow like they would on the job, and pay attention to how they think through problems. The goal isn't to stump them with trivia, it's to see if they can do the work you're hiring them to do. Your interview process should mirror your actual work environment as closely as possible.
