6 DevOps Career Paths with 6-Figure Potential

Key Takeaways

Something very interesting came up in my conversations with students and engineering professionals who want to break into DevOps. There's this moment of overwhelm that hits when they realize DevOps isn't just one clearcut role because they see job listings for cloud engineers, SRRES, platform engineers, DevOps engineers with the skill set of GitOps, DevSops, MLOps, all requiring DevOps skills and naturally the confusion sets in. So when learning DevOps, they ask themselves, wait, which one am I actually training for? Where does this DevOps journey actually lead me? And that's why in this video, I'm going to map out all of the six DevOps career path in detail and their differences so that you know exactly which career path you want to follow. Because this uncertainty creates real anxiety. You are investing time and money into learning these skills, but without a clear destination or clear idea of where you're heading. And it's hard to stay motivated or make strategic decisions about what to learn next. And that's why mapping out this career path matters so much. When you understand the specific branches that stem from your DevOps foundation, you can align your learning with your natural strength and interests. So for example, someone who loves diving deep into security incidents and investigating will thrive in a completely different role than someone who gets energized by building tools for other developers. For example, now to make this more actionable for you, we actually created a complete DevOps road map that describes all the relevant DevOps tools and concepts in a big picture overview so that you can easily grasp everything at once. And this can be your perfect learning guide as a foundation for every single one of these six career path that we are about to talk about. So grab it from below and let's dive in so that I can help you match your personality, working style, and interests to the right DevOps specialty so you can learn with a clear goal and build a career that genuinely fits who you are. Path number one, DevOps engineer. Let's start with the most direct path. Becoming a DevOps engineer. While DevOps began as a concept and cultural movement, not a job title, it quickly evolved into a distinct role as companies needed specialists to implement these practices. on top of the classical software engineering or system administration work. So as a DevOps engineer, you're basically the person who builds and maintains the pipeline that gets code from developers laptops into production as well as someone who makes sure that deployed application is up and running and properly monitored for any issues. You are the bridge between people who write the code and the systems where it runs. In a typical week, as a DevOps engineer, you might fix a broken Jenkins pipeline that's preventing the marketing team from updating their website. Write a script that automatically creates test environments whenever a developer opens a pull request. Maybe help a database team set up automatic backups for their production systems. You may be working on debugging why containers are crashing in Kubernetes cluster and implement a fix that prevents it from happening again. So you will do well in this role if you were naturally curious about both sides of software delivery. Not just how code is written but also how it runs in production. Personally, when I was accidentally pushed into this role as a senior software engineer back then, it was a dream come true for me because it aligned exactly with my interests and natural abilities. Immediately after, I moved into full-time DevOps engineer role because I enjoyed it so much compared to software development. Now note that in this role you need to be very comfortable jumping between different technical problems and also explaining complex issues in very simple terms to different teams. The pay for this role is excellent typically between 110 170k with senior roles exceeding even 180k at top companies. So you can say it is a pretty lucrative career path. Now while some prior IT experience helps either as a developer or in operations, many successful DevOps engineers actually started in one area and gradually learned the other side. The most important thing here is being able to understand and communicate with both development and operations teams. Path number two, cloud engineering. Think of cloud engineers as the architects of the digital sky. They're the ones who take all those DevOps automation skills and apply them to building and maintaining the entire cloud infrastructure that modern applications run on. So once you've mastered core DevOps skills like infrastructure as code, CI/CD pipelines, containerization and container orchestration, becoming a cloud engineer is basically a natural evolution. You're essentially zooming out from the specific application deployments to designing and managing the entire cloud environment. Your DevOps automation experience becomes the foundation for orchestrating entire cloud ecosystems instead of just individual services. In a typical week, as a cloud engineer, you might design a multi-reion AWS architecture for a new product launch, making sure that it meets both performance and disaster recovery requirements of your company. You may also be writing Terapform modules that standardize how your company deploys databases, complete with backup policies and monitoring for those services. You may be debugging some mysterious spike in cloud costs, discovering an orphaned Kubernetes cluster costing $4,000 a month and implementing some governance to prevent future waste. Or you may be creating a migration plan to move legacy applications from onremise to cloud environment with minimal downtime. You will thrive in this role if you enjoy solving complex puzzles and also have good spatial reasoning and can balance technical requirements with business constraints. The ideal cloud engineer is both creative but also methodical. someone who is able to imagine the innovative solutions while implementing them with very specific precision. The salary range for cloud engineers is slightly less than for DevOps engineers, but it's still pretty high. In the US, it actually starts already at six figures and increases pretty rapidly with seniority level and expertise. Path number three is platform engineering. Platform engineers are like tools smmith of the tech world. They build the digital workbenches that other developers use to create and ship products faster and more reliably. This role is a direct application of the force multiplier aspect of DevOps. So once you understand how CI/CD pipelines, infrastructure automation and containerization work from your DevOps expertise, platform engineering lets you package those workflows into userfriendly systems for the entire company and the development teams in the company. So, you're taking the same DevOps tools and principles you've learned and creating standardized self-service versions that scale across an entire organization. A week in this role might include meeting with software developers to understand their pain points. then going and creating a self-service portal where they can provision new deployment environments for their applications without filing IT tickets and waiting for operations team to create that deployment environment. Or uh you could be building a custom CLI tool that standardizes how developers across the company deploy microservices reducing the deployment effort and errors by 60%. Let's say or you could be creating an internal app store of pre-approved services and templates with one-click deployment. You could be working with security engineers to bake in security best practices in application deployment workflows or CI/CD pipeline templates. Or you could be designing documentation and conducting workshops to help developers adopt your new tooling. Now you will excel in this career if you have high empathy, very good communication skills and enjoy creating systems that other teams and engineers will use that basically standardize and make complex processes simple. Platform engineers must understand both technical capabilities and the human workflows. So they need to balance what's technically possible and what's actually usable in terms of value. Think of it this way. DevOps engineers automate things and make them efficient in a project. Platform engineers take those DevOps engineer tasks and standardize them for the whole organization not just one project and automate the DevOps engineers work itself to some extent. So you can imagine how valuable these professionals are for companies and that's why in the US for example you can expect salaries for platform engineers between 160k up to 270k for experienced engineers. And important to note that the senior level DevOps engineer skill set which includes the administration side of tools not just the usage is exactly what you need for platform engineering. You need to understand not just how to use the already set up and preconfigured tools but more importantly actually how to set them up in the first place. And this is exactly what we teach in DevOps boot camp. the full spectrum of administration and usage of every single tool that is relevant for DevOps with just one exception which leads us to the next career path. Dev Sec Ops engineers are the security guardians who make sure that the cool stuff that we built isn't vulnerable to attacks. So they blend security expertise with automation to protect systems without slowing down development and deployment. This path takes your DevOps automation and CI/CD pipeline knowledge as well as infrastructure as code skill set and adds a crucial security dimension to it. So once you understand how to automate deployments and infrastructure from your DevOps experience, Dev Secops involves injecting security checks and controls into those same workflows. You're essentially taking this shift left mentality or concept of DevOps and applying it specifically for security measures. So your week as a devsops engineer might include the following tasks. implementing a tri scanning in your CI/CD pipeline, then working with development teams to remediate the 24 critical vulnerabilities that the tool discovered in container images, or creating a GitOps workflow for security policies, allowing infrastructure to automatically enforce rules like no public S3 buckets allowed or all databases must be encrypted. You could also be building a secrets rotation system that automatically updates API keys across your Kubernetes environments every 30 days. Or you could be working with compliance team to identify industry specific compliance rules and then implement continuous automated checks for those compliance rules and then on top of it work with engineers to fix the discovered compliance issues. Now you will thrive in this role if you enjoy investigative work, have an adversarial thinking style and also someone who can balance security with practicality. The best defineers aren't just security focused. They are also educators who can help teams understand why certain practices matter. As you can imagine, security and specifically security automation is extremely valuable for companies. So these professionals often command extremely high salaries. Plus, this is actually a senior DevOps engineer position because Dev Sec Ops implies that you already have solid DevOps skills to add security skill set on top of that which is exactly what we teach in our Dev SecR as the firefighters and fire prevention specialists of the tech world. They make sure that critical systems stay up and running and when things inevitably at some point break they are the calm problem solvers who get everything back on track. SR is essentially taking the monitoring observability and automation aspects of DevOps and making them the primary focus. So your DevOps experience with infrastructure automation, containerization against CI/CD gives you the foundation to understand how systems are built and deployed. And as an S sur you were then adding a deeper layer of expertise in reliability patterns, monitoring systems, incidents response processes to make sure that those systems remain healthy in production. But in case something happens, you can remediate everything in the shortest time possible, reducing the impact and the damage to the minimum. Now, a typical week of an S sur might include creating a custom Prometheus alert for database connection that triggers before your application starts throwing errors, which gives your teams precious minutes to respond early. Or it could be conducting a postmortem after a 45minut series outage identifying not just the technical trigger like a misconfigured load balancer whatever caused the issue but also the process failures that allowed it to reach production. It could be another task maybe building an automated remediation system that detects and restarts the stuck batch processes for example without human intervention or it could also be running a chaos engineering experiment where you deliberately take down a critical service to verify and check that your failover systems work as designed. So as you see most of the focus is set on the reliability of the systems as well as creating really good remediation and backup plans in case something goes wrong. Now you will excel as an S sur if you have exceptional diagnostic abilities. You are someone that can think clearly during incidents and doesn't panic, remains calm under stress and someone who enjoys optimizing systems for reliability. This role actually demands both technical depth but also the the emotional resilience to handle high pressure situations. Talking about pay, according to glass door, s sur engineer compensation typically range from somewhere around 130k all the way up to 210K for senior positions at top companies. And finally, path number six, MLOps. So, finally, a role that is becoming extremely demanded because of rise of AI is MLOps engineering. Simply explained, MLOps engineers are the bridge builders connecting the data science world with production systems. They make sure that the cool AI models don't just work in the research notebooks, but can actually run reliably in the real world applications. So this specialty takes your DevOps pipeline knowledge and Kubernetes skills and applies them specifically to machine learning workflows. The CI/CD, infrastructure automation, the containerization principles that you've learned in DevOps here form the foundation for building machine learning specific deployment pipelines. You're essentially creating DevOps for data science. So you're taking the same automation principles but adapting them for the unique challenges of deploying and monitoring machine learning models. Again to go through a standard week of an envelopes engineer and how it would look like. You may be building a pipeline that automatically retrains a recommendation engine when performance metrics drop below thresholds then perform AD tests on the new model against the current one. Or you may be creating a feature store that standardizes how customer behavior data is processed and made available to data scientists, reducing model development time from weeks to days. Another task may be implementing a monitoring system that detects when a fraud detection model's predictions start drifting from historical patterns, alerting the team to potential data quality issues. Or another task would be optimizing a GPU cluster for distributed training, reducing the time to train a large language model from, I don't know, 14 days to 3 days. So you will thrive in MLOps if you enjoy interdisiplinary work and have both analytical and systems thinking abilities and you are someone that can bridge the gap between data science and operations tips. So this role requires translating between different technical domains but also solving pretty novel infrastructure challenges. A typical salary for MLOps engineers ranges from 130K for entry- levelvel engineers by the way to 200,000 and higher for experienced professionals. Now, here is what I find fascinating. All six of these path start with the same core DevOps foundation. So the same skills in automation, cloud platforms, CI/CD pipelines, Kubernetes and container orchestration in general that we teach in our DevOps boot camp form the foundation for every single one of those career paths. So these core skills are your launch pad from where you can specialize based on what interests you the most or depending on which career you think will be the most lucrative and highly demanded for decades to come. Which by the way is a very legitimate reason to choose one of the careers. And unlike many tech specializations that may become obsolete, these roles, every single one of them are becoming more important every year. The more complex our systems are becoming, the more we need experts who can make these systems reliable, secure, efficient, automated with less human error and so on. So take some time to reflect on which of these descriptions resonated most with you and ask yourself did you feel a spark of excitement when I described the platform engineer building tools for others or did you lean forward when I talked about the surre troubleshooting complex system issues. So think about which one of the task descriptions and needed skills triggered your interest the most. And that natural interest can be your compass that you can follow so that you not only build valuable skills but find work that genuinely engages and interests you. So which path interested you the most? I'll be very curious to know. So, drop a comment below and I will share specific resources to help you get started for that career path. And as always, I hope this was helpful. Thanks for watching and I'll see you in the next