The Whiteboard Rebellion: Why DevOps Engineers Are Pushing Back
I still remember the moment—the dry-erase marker in my hand, the blank whiteboard staring back, and the interviewer asking me to reverse a binary tree. For a Platform Engineering role. In 2026. My brain did that thing where it simultaneously tried to recall algorithms from a college class I took a decade ago while screaming internally: "When will I ever need this to debug a Kubernetes pod stuck in CrashLoopBackOff?"
If you've been in the DevOps hiring space recently, you've felt this tension. The original Reddit post that sparked this discussion hit 556 upvotes and 140 comments for a reason—it's a raw nerve in our community. Engineers are tired of proving they can solve algorithmic puzzles that have zero bearing on whether they can design resilient systems, troubleshoot production issues, or build effective platform tooling.
This isn't just about interview frustration. It's about a fundamental mismatch between how we assess candidates and what they actually do in their jobs. And in 2026, with DevOps practices more mature than ever, this disconnect is becoming increasingly costly for both companies and engineers.
The Great Misalignment: Why LeetCode Doesn't Translate to DevOps Success
Let's start with the obvious question: Why do companies keep using algorithmic puzzles for DevOps interviews? From what I've seen, it usually comes down to a few factors. Some hiring managers are copying what FAANG companies do without understanding why those companies do it. Others are using it as a "filter"—a way to reduce candidate volume. And honestly? Some just don't know what else to ask.
But here's the problem: DevOps and platform engineering require a completely different skillset than traditional software development. Sure, there's overlap—you need to understand code, logic, and systems. But the day-to-day work is fundamentally different.
Think about it. When was the last time you needed to implement Dijkstra's algorithm to troubleshoot a database connection pool issue? When did memorizing sorting algorithms help you optimize a CI/CD pipeline that's taking 40 minutes? The skills that matter in DevOps—systems thinking, debugging under pressure, understanding trade-offs, communication across teams—aren't tested by solving puzzles on a whiteboard.
I've interviewed dozens of engineers over the years. The best ones aren't necessarily the ones who can solve LeetCode hard problems in 30 minutes. They're the ones who can look at a complex system, ask the right questions, and methodically work through problems. They understand that real-world systems are messy, documentation is often incomplete, and the "right" solution depends on context.
What Actually Matters: The Real Skills DevOps Engineers Need
So if algorithmic puzzles aren't assessing the right skills, what should we be looking for? Based on conversations with hiring managers and engineers across the industry, here are the competencies that actually predict success in DevOps roles:
Systems Thinking and Debugging
Can they trace a request through a distributed system? When presented with a 502 error from an Nginx ingress controller (like in the original post), do they know where to start looking? The best DevOps engineers I've worked with have a mental model of how systems interact. They understand that a problem at the application layer might actually be caused by a network policy, or that a slow API response might trace back to database connection limits.
This isn't about memorizing solutions—it's about having a methodology. Good engineers ask questions like: "What changed recently?" "What are the error rates across different services?" "What do the metrics show?" They know how to use observability tools, read logs effectively, and correlate data from different sources.
Practical Automation and Tooling Knowledge
DevOps is fundamentally about automation. But not the kind of automation that involves implementing complex algorithms from scratch. We're talking about knowing when to use Terraform versus Pulumi, understanding how to structure Helm charts effectively, or knowing the trade-offs between different CI/CD platforms.
Here's a reality check: Most DevOps work involves gluing together existing tools and platforms. It's about understanding APIs, working with cloud provider SDKs, and writing scripts that are maintainable and reliable. The skill isn't in implementing a perfect sorting algorithm—it's in knowing which existing library or service to use, and how to integrate it effectively.
Communication and Collaboration
Remember that question from the original post: "How do you handle a dev who refuses to follow the CI/CD flow?" That's not a trick question—it's a real scenario that happens constantly. DevOps engineers work at the intersection of development and operations, which means they need to communicate effectively with both sides.
The best platform engineers I know are diplomats as much as they are technicians. They can explain technical constraints to developers in ways that make sense. They can advocate for operational requirements without sounding like gatekeepers. And they can document systems clearly so that everyone understands how things work.
Better Questions: What to Ask Instead of LeetCode Problems
Okay, so we know what skills matter. How do we actually assess them in interviews? Here are some alternatives that actually test relevant competencies:
Scenario-Based Questions
Instead of "reverse a binary tree," try something like: "You get paged at 2 AM because the production payment service is returning 500 errors. Walk me through how you'd investigate." Let the candidate ask clarifying questions. Do they check metrics first? Look at recent deployments? Examine logs? The process matters more than getting to a specific answer.
Or how about: "A developer comes to you saying their application is slow in the staging environment but fine locally. What would you investigate?" This tests their understanding of environment differences, networking, and systematic troubleshooting.
Architecture and Trade-off Discussions
The original post mentioned asking about service mesh trade-offs—that's perfect. Present a real scenario: "We need to implement service-to-service authentication in our Kubernetes cluster. What are our options, and what are the trade-offs of each?"
This isn't about having the "right" answer. It's about showing they understand the landscape. Do they mention Istio versus Linkerd? Do they consider the operational overhead? Do they think about developer experience? These discussions reveal how they think about systems and make decisions.
Practical Exercises (That Actually Make Sense)
If you want to see someone code, give them a realistic task. "Here's a broken Terraform module that's supposed to create an S3 bucket with proper encryption and logging. What's wrong with it, and how would you fix it?"
Or: "Write a script that checks if all our production pods are running the correct image tag." These are tasks they might actually do on the job. They test practical coding skills without requiring algorithm memorization.
Some companies are even moving to "take-home" projects that simulate real work. The key is making them reasonable—not 20-hour marathons, but 2-4 hour tasks that show how someone approaches real problems.
The Hiring Manager's Dilemma: Balancing Signal with Practicality
I get it—hiring is hard. You need some way to filter candidates, and you want to avoid false positives. But here's what I've learned from being on both sides of the table: LeetCode gives you signal, but it's the wrong signal.
You're selecting for people who are good at algorithmic puzzles, not people who are good at DevOps. And those are different skillsets. I've seen brilliant puzzle-solvers who couldn't debug their way out of a paper bag when faced with a real production issue. And I've seen engineers who would fail a medium-difficulty LeetCode problem but could diagnose and fix complex distributed systems issues in minutes.
The irony? By using LeetCode for DevOps roles, you're probably filtering out exactly the people you want. The engineers who are passionate about infrastructure, automation, and reliability are often spending their time learning Terraform, Kubernetes, and cloud platforms—not practicing binary tree rotations.
And let's talk about diversity for a moment. Algorithmic interviews have well-documented bias issues. They favor people who have time to practice (often recent graduates or people without caregiving responsibilities) and people from certain educational backgrounds. If you want to build diverse, effective teams, you need assessment methods that actually test job-relevant skills.
What Candidates Can Do: Navigating the Current Landscape
Okay, so the system isn't perfect. What should you do if you're interviewing for DevOps roles in 2026? Here's my practical advice:
First, ask about the interview process early. When a recruiter contacts you, say something like: "I'm excited about this opportunity. Can you tell me about the technical interview process? What kinds of problems or scenarios should I expect?" If they mention algorithmic puzzles, you can gently push back: "I want to make sure I'm prepared. For a platform engineering role, will the interview focus more on systems design and practical problem-solving?"
Second, redirect when you can. If you do get an algorithmic question in an interview, try to connect it back to real-world DevOps scenarios. After solving (or attempting to solve) the problem, you might say: "That was interesting. In my experience with distributed systems, I'm more often dealing with problems like [real example]. How does the team approach those kinds of issues here?"
Third, showcase your practical skills. Your resume and portfolio should highlight real work. Instead of just listing technologies, describe what you built and the impact it had. "Implemented GitOps workflow that reduced deployment failures by 40%" tells me more than "Experience with ArgoCD."
And honestly? Consider whether you want to work somewhere that doesn't understand what DevOps engineers actually do. An interview process that relies heavily on LeetCode for a DevOps role might indicate deeper issues with how the company thinks about infrastructure and operations.
The Future Is Practical: Where DevOps Hiring Is Heading
Here's the good news: The industry is starting to shift. More companies are realizing that traditional coding interviews don't work for infrastructure roles. We're seeing the rise of role-specific interviews that actually test relevant skills.
In 2026, I'm seeing more companies adopt these practices:
- Structured behavioral interviews focused on past experiences with incidents, migrations, and cross-team collaboration
- System design interviews tailored to infrastructure ("Design a CI/CD pipeline for a microservices architecture" rather than "Design Twitter")
- Practical coding exercises using actual tools (Terraform, Ansible, Kubernetes manifests) rather than algorithmic puzzles
- "Working sessions" where candidates pair with current engineers on real (but sanitized) problems
The companies that get this right are seeing better hiring outcomes—higher offer acceptance rates, better retention, and more effective teams. They're hiring engineers who can actually do the job, not just solve puzzles.
Common Questions (and Real Answers)
"But don't we need to test coding ability?"
Absolutely. But test the kind of coding DevOps engineers actually do. Give them a broken configuration file to fix. Ask them to write a script to parse logs. Have them debug a problematic Dockerfile. These test coding skills in context.
"What about junior candidates who don't have much experience?"
For junior roles, focus on fundamentals and learning ability. Ask about projects they've built. Give them a simple scenario and see how they think through it. Look for curiosity and systematic thinking rather than specific knowledge.
"Isn't LeetCode at least a consistent way to compare candidates?"
Consistent, yes. Relevant, no. It's like comparing basketball players by how fast they can run 100 meters. Sure, speed matters in basketball, but it's not the only thing that matters, and it doesn't tell you how well they can actually play the game.
"What if we need someone who can write complex algorithms for our platform?"
Then you're probably not hiring for a traditional DevOps role—you're hiring for a platform developer. Be clear about what you need. Most DevOps engineers work with existing tools and write glue code, not implement complex algorithms from scratch.
Moving Forward: A Call for Sanity in DevOps Hiring
Look, I get why companies default to LeetCode. It's a known quantity. There are practice sites, study guides, and a whole industry built around it. Changing interview processes takes work. But in 2026, we should know better.
DevOps and platform engineering have matured as disciplines. We have a better understanding of what skills actually matter. We know what separates good engineers from great ones. And it's not their ability to invert a binary tree on a whiteboard.
If you're hiring for DevOps roles: Take a hard look at your interview process. Ask yourself: "Are we testing what actually matters for this role?" Talk to your current engineers about what skills they use daily. Build an interview process that reflects reality, not convention.
If you're interviewing for these roles: Advocate for yourself. Ask questions about the process. Showcase your practical skills. And remember—an interview is a two-way street. A company that uses irrelevant interview methods might have other issues with how they value and understand infrastructure work.
The original Reddit poster walked out of that interview. I don't blame them. But more importantly, I hope the hiring manager learned something. Because in 2026, we should be past this. We should be asking better questions. We should be building better processes. And we should definitely stop with the LeetCode for DevOps roles.
