Issue #1: Where Do You Really Stand? The DevOps Python Skills Self-Assessment (From Junior to Senior Series #1/5)

You're scrolling through yet another DevOps job posting, and there it is again: "Strong Python skills required." And so does the next, and the one below... Oh man, when will I ever land that job?

Let's take a step back. What does this even mean? How do you know if your Python skills are junior-level, mid-level, or senior-level in the eyes of hiring managers?

Here's the uncomfortable truth: most DevOps engineers have no idea where they actually stand. They might be able to write Python scripts that work, but they don't know if those scripts would impress a senior engineer or get them laughed out of a code review.

This isn't just about job hunting. It's about understanding exactly what skills you need to develop to advance your career. With the DevOps market growing 20% annually and 37% of IT leaders reporting DevOps skills as their biggest gap, knowing your current level and the path forward has never been more critical.

This article is part of my series From Junior to Senior: Python Skills That Actually Matter in DevOps. Over the next six articles, I'll break down important Python skills that separate junior, mid-level, and senior DevOps engineers. I'm not going to claim that I'll list all of them, but I'll discuss important ones that I've observed in DevOps engineers at all levels. Don’t want to miss it?

But first things first, let's start by getting the baseline right. Let's spend a few minutes to explore where you're starting from.

Most technical skills assessments are useless for career planning. They test syntax knowledge or algorithm skills that have little bearing on day-to-day DevOps work. You might ace a quiz about Python data structures but still write scripts that are sh crash in production.

What matters in DevOps isn't whether you know the difference between a list and a tuple. It's whether you can write automation that survives the chaos of real production environments. It's whether your code helps or hinders your teammates. It's whether you can debug issues quickly when systems are down and everyone is stressed.

Instead of testing theoretical knowledge, this assessment evaluates how you approach real DevOps scenarios. Each section presents situations you've likely encountered (or will encounter) and asks you to honestly evaluate your current approach.

There are no trick questions. The goal isn't to make you feel bad about gaps in your knowledge: it's to give you a clear, honest picture of where you stand and what you need to work on next.

Be brutally honest with yourself. Nobody benefits from inflating your self-assessment. I repeat: nobody, not even yourself, benefits from an inflated ego. Your career progression depends on accurately understanding your current capabilities and the gaps you need to fill.

Before diving into the assessment, let's clarify what we mean by junior, mid-level, and senior in the DevOps context. Just a summary, we'll dive deeper into these in later articles.

Junior engineers can write Python scripts that solve specific problems, but their solutions often work only under ideal conditions. They rely heavily on external resources (Stack Overflow, AI assistants, documentation) to solve problems and may not fully understand why their solutions work.

The hallmark of a junior engineer isn't lack of intelligence; it's lack of experience with how things break in the real world. Their scripts work on their laptop but may fail in different environments, under load, or when edge cases occur.

Mid-level engineers write Python code that's reliable and maintainable. They anticipate common failure scenarios and design their solutions accordingly. They can work independently on most tasks and contribute meaningfully to technical discussions.

Mid-level engineers understand not just how to make something work, but how to make it work consistently across different environments and under various conditions. They've been burned by production issues enough times to build defensive programming habits.

Senior engineers think in systems, not just scripts. They design solutions that integrate well with other tools, are easy for teammates to understand and extend, and prevent entire classes of problems from occurring. And they might happen to use Python to implement these solutions reliably.

Senior engineers multiply their impact through the quality of their code and their influence on team practices. Their solutions become templates that others follow, and their architectural decisions shape how the team approaches problems.

If this sounds too far fetched, you're yet to work with a real senior engineer (forget the titles now, I'm talking real skill and mindset). They do exist, yes, and they make a hell of a difference.

Alright, now that we have a high-level overview of what is what, let's jump into the self-assessment. For each scenario, choose the response that most closely matches your current approach and capabilities. Don't choose what you think you should do; choose what you actually do in practice. And make sure to share your results in the poll below; it will also help me craft more relevant content for you 😊

A. I search for deployment scripts online, find one that looks close to what I need, and modify it until it works for my specific case. If it breaks, I debug it by adding print statements and searching for error messages. Ah, I delete intermediary resources by hand if the script crashes midway.

B. I write a script with proper error handling, logging, and configuration management. I test it in a staging environment first and include rollback functionality in case something goes wrong.

C. I would design a deployment system that handles different application types, environments, and failure scenarios. I'd include monitoring integration, automated testing, and make it easy for other team members to extend for new use cases. I also make sure to implement it gradually, so that it can deliver value even though not all features are implemented.

A. I add print statements throughout the code to see where it's failing, then search online for solutions to the specific error messages I'm seeing. Or I just throw the piece of code to AI, ask it to solve the problem, and hope that the proposed solution works longer the current one.

B. I check the logs (because I included proper logging when I wrote it), examine what's changed in the environment, and use the error handling I built in to understand what specifically went wrong.

C. I use the monitoring and alerting I built into the script to understand not just what failed, but why. I'd have enough contextual information in my logs to quickly identify whether it's a code issue, environment change, or external service problem.

A. I walk through the code line by line, explaining what each part does. I might need to look up some parts myself to remember exactly how they work. By the way, it's good that my colleague didn't do the obvious and threw the code snippet into AI, because not even it would be able to explain it 😅

B. I explain the overall approach, the key functions, and the reasoning behind major design decisions. I can answer questions about error handling and configuration without needing to look at the code.

C. I explain the architecture, design tradeoffs, and how it fits into our larger automation ecosystem. I can discuss alternative approaches we considered and why we chose this one, plus how it could be extended for future needs. I also point towards the ADR we created when discussing the alternative approaches, as this can be helpful to my colleague to gather useful information.

A. I use a requests library example I find online, maybe add a try/except block if I remember to, and hope the API is reliable.

B. I implement retry logic with exponential backoff, timeout handling, and proper error messages. I log API response times and failures for monitoring.

C. I build a resilient API client with circuit breaker patterns, rate limiting awareness, and graceful degradation. I include metrics collection and design it as a reusable component for other scripts.

A. I include comments in the script about what needs to be changed for different environments, or maybe create separate versions for each environment. I'd keep extending the README with edge-cases and obscure bash commands to handle them.

B. I use environment variables and configuration files to handle differences between environments. I include validation to ensure the script has the configuration it needs.

C. I design a configuration management system that supports environment inheritance, validation, and secure secret handling. I include environment detection and appropriate defaults for different deployment contexts.

A. I might point out obvious bugs or suggest different approaches based on examples I've seen online, but I'd be hesitant to make major suggestions since I'm not sure of the best practices.

B. I provide specific feedback on error handling, code organization, and potential edge cases. I suggest improvements and explain the reasoning behind my recommendations.

C. I evaluate the script's architecture, maintainability, and integration with our existing systems. I provide feedback that helps the author improve not just this script, but their overall approach to similar problems.

A. I check on them manually periodically, maybe add some print statements to show they're running, and hope someone tells me if they break. And why not create a dashboard in our internal monitoring tool that shows a green light if the exit code is zero?

B. I add structured logging and basic monitoring. I set up alerts for failures and track key metrics like execution time and success rates.

C. I implement comprehensive observability with metrics, logs, and traces. I include business logic monitoring, performance benchmarks, and automated anomaly detection.

A. I call in sick 😂 I'm joking... But I do feel overwhelmed and start by adding debug prints to see what's happening. I rely heavily on senior team members to guide the debugging process.

B. I use the logging and monitoring I built in to quickly identify what the script was doing when the issue occurred. I am able to determine if the script was the cause or just correlated with the timing.

C. I quickly isolate whether the issue is in my script using the observability I built in, and if so, I now have enough context to either fix it immediately or safely disable it while I investigate further.

Count how many A's, B's, and C's you selected:

Mostly A's: Junior Level

You're in the early stages of your DevOps Python journey. You can write scripts that work, but you haven't yet developed the defensive programming habits that prevent production issues. This is completely normal—everyone starts here.

Mostly B's: Mid-Level

You write reliable, maintainable Python code and think about failure scenarios. You have solid engineering practices and can work independently on most tasks.

Mostly C's: Senior Level

You think architecturally and design systems that other engineers can build upon. Your solutions are robust, observable, and integrate well with larger systems.

Mixed Results

If you have a mix of responses across levels, that's actually quite common. You might be strong in some areas (like error handling) but weaker in others (like system design). This granular view is valuable, as it shows you exactly which skills to focus on developing.

Understanding your current level isn't about judgment; it's about clarity so that you effectively work on the skills that will bring the most value to your carreer. Here's what each level typically means in terms of career progression:

You're building foundational skills that will serve you throughout your career. Focus on writing more robust code and developing debugging skills. The good news: this is where you have the most room for rapid improvement.

Most importantly, start thinking beyond "does it work?" to "will it work reliably in different conditions?" This mindset shift is the key to advancing to mid-level.

You have solid technical skills but may need to develop more systems thinking and architectural awareness to advance to senior roles. Consider how your solutions fit into larger workflows and how other engineers interact with your code.

The key transition here is from solving individual problems to solving classes of problems. Start thinking about how your solutions can be extended and reused.

You're likely ready for senior roles or already in one. Focus on multiplying your impact through mentoring, creating reusable systems, and influencing team practices.

Remember that senior engineers are often evaluated more on their impact on others than on their individual technical contributions.

Here's some context that might surprise you: Recent market data shows that 60% of DevOps roles require senior-level experience, while only 5% target junior positions. This doesn't mean you should despair if you're junior; it means there's significant opportunity for those who develop the right skills.

The skills gap is real. Organizations are struggling to find DevOps engineers who can write reliable Python automation. This creates tremendous opportunity for engineers who focus on developing the right capabilities.

But here's the encouraging part: the gap between levels is often smaller than people think. It's not about learning completely new technologies: it's about developing better habits and deeper thinking about the problems you're already solving.

Now that you know where you stand, the next articles in this series will show you exactly how to advance to the next level:

Before the next article comes out, I want you to do one thing: pick your worst Python script (the one you're most embarrassed about, yes) and spend 30 minutes improving it using the principles from your target level.

Don't try to rewrite everything. Just pick one aspect (maybe error handling, or logging, or configuration) and make it better. Do some research. Use AI as your learning and mentoring buddy. Grow from the experience. This exercise will give you a concrete feel for the gap between where you are and where you want to be.

In the next article, we'll explore the specific skills that transform script-writers into reliable automation engineers. We'll cover the fundamental mindset shifts that separate junior engineers who can make things work from mid-level engineers who make things work consistently.

Until then, have a wonderful time and happy automating!