Your roadmap to landing a DevOps job in 2025
First published: Friday, January 24, 2025 | Last updated: Friday, January 24, 2025Master essential skills, tools, and strategies to kickstart your DevOps career with this 2025 roadmap for success.
What is the role of a DevOps engineer?
A DevOps job role involves bridging the gap between development and operations teams to ensure faster, reliable, and efficient software delivery. If you need more understanding about what is DevOps, their primary responsibilities include:
- Collaboration: Facilitating communication between development teams and IT operations to enhance productivity and efficiency.
- Automation: Implementing tools and processes to automate manual tasks, such as code integration, testing, deployment, and infrastructure management.
- Continuous Integration and Continuous Delivery (CI/CD): Setting up CI/CD pipelines to ensure rapid and reliable software releases.
- Observability, monitoring, and performance optimization: Continuously monitoring application performance and infrastructure health, and analyzing systems to identify areas for improvement.
- Infrastructure as Code (IaC): Using code to manage and provision infrastructure, making it easier to replicate and manage.
- Troubleshooting: Identifying and resolving issues that arise during development and operations phases.
Who can be a DevOps engineer?
Anyone with dedication and passion can become a DevOps engineer. If you’re from systems administration, IT operations, software development, QA engineer you’re already on the right track. Experience in technical support or production support can also make the transition easier. DevOps is about bridging gaps between teams and automating workflows, which can be learned with the right mindset. With continuous learning and problem-solving skills, you can excel in this dynamic field.
How long does it take to become a DevOps engineer?
Becoming a DevOps engineer in 4-6 months is achievable with focus and consistent effort. Dedicate time each day to learning and practicing, and you’ll quickly gain the skills and confidence needed for the role. With a structured plan and strong commitment, you can successfully reach this career milestone.
Where to start to become a DevOps engineer?
Initially, when you look at the DevOps roadmap, it can create a sense of fear because you need to learn a lot of tools and technologies. These tools will change frequently, but you need to be strong in the underlying concepts. For every problem, there are plenty of tool options to solve it, but it’s important to focus on mastering the concepts. Most tools share 50-60% similarities, so understanding the core principles is key.
The DevOps skills consists of several critical stages to ensure smooth software delivery. It begins with operating systems, where systems are configured and optimized for performance. Next, containerization & container orchestration tools like Docker, Kubernetes, and Helm are used to package and manage applications. Continuous Integration & Continuous Delivery (CI/CD) automate the process of integrating and deploying code. Then, Infrastructure as Code (IaC) tools help manage infrastructure using code, ensuring consistency across environments. Observability tools are essential for monitoring system performance and health, providing valuable insights for improvement. The lifecycle concludes with continuous feedback and optimization to drive better efficiency.
Operating systems
Linux is the backbone of modern DevOps practices, providing the stability, flexibility, and scalability required for managing complex infrastructure. Its open source nature and widespread adoption make it the operating system of choice for running most DevOps tools like Docker, Kubernetes, and Ansible. Mastering Linux allows DevOps engineers to automate tasks, manage servers, and troubleshoot systems efficiently. With its robust command line interface and powerful scripting capabilities, Linux empowers professionals to build, deploy, and maintain reliable and high-performing applications seamlessly.
The key Linux topics every aspiring DevOps engineer must learn
| Topic | Details |
|---|---|
| Command line operations | Navigating directories and managing files efficiently using basic commands like ls, cd, cp, etc. |
| Bash scripting | Automating repetitive tasks and workflows with shell scripts to enhance productivity. |
| User and group management | Ensuring secure access control by managing users, groups, and permissions. |
| Filesystems and partitioning | Managing storage, understanding partitioning concepts, and working with file systems (ext4, xfs). |
| Process and service management | Monitoring system performance, managing processes, and ensuring that services run properly. |
| Networking basics | Configuring IP addresses, firewalls, and using tools like curl, ping, and netstat. |
| Package management | Installing, updating, and removing software packages efficiently using package managers (apt, yum). |
| Secure Shell (SSH) | Managing secure remote access to systems for administration and troubleshooting. |
These topics are essential for managing Linux-based infrastructures and automating tasks effectively in DevOps.
Software design and architectures
Understanding software design is fundamental in DevOps, as it shapes how applications are built, deployed, and managed. Exploring Monolithic architecture, Service Oriented Architecture (SOA), and Microservice architecture reveals the evolution of application development. These concepts highlight the growing importance of containerization in addressing modern scalability and deployment challenges.
Evolution of architectures
- Monolithic architecture: Understand the limitations of tightly coupled components in a single codebase, such as difficulty in scaling and deploying updates independently.
- Service Oriented Architecture (SOA): Learn how modular services communicate over networks, offering better scalability and reusability but introducing challenges in integration and complexity.
- Microservice architecture: Explore how breaking applications into smaller, independently deployable services improves scalability, resilience, and flexibility.
These topics help you understand why containerization is crucial in modern software design. Containers provide isolated environments for deploying microservices, addressing the complexities of interdependencies, scalability, and portability. They ensure consistent application behavior across different environments, simplifying deployment, and enabling effective orchestration using tools like Docker, Kubernetes, and Helm.
Programming
Having at least one programming language in your skillset is vital for any DevOps engineer. Start with Bash scripting, as it’s fundamental for automating tasks and managing Linux-based environments. From there, learn Python, a versatile language widely used for automation, API integrations, and writing scripts for tools like Ansible. If you’re working with Infrastructure as Code (IaC) tools like Chef, Ruby can be a great addition to your toolkit. Mastering even one of these languages will significantly enhance your ability to automate processes, manage configurations, and contribute effectively to DevOps projects.
Version Control System (VCS)
Version Control System (VCS) is essential for tracking changes, enabling collaboration, and maintaining a reliable history of your project source code. Tools like Git allow teams to work concurrently on the same codebase while avoiding conflicts and ensuring traceability. Hosting platforms like GitHub, GitLab, and Bitbucket complement VCS by providing centralized source code repositories, issue tracking, and CI/CD pipelines.
These platforms facilitate seamless collaboration, code reviews, and version management, making them indispensable in a DevOps environment. With version control and hosting, DevOps teams can efficiently manage codebases, ensure smooth deployments, and maintain high-quality software development practices.
Key Git concepts every DevOps engineer should know
| Topic | Details |
|---|---|
| Git repository | Initializing a repository, ignoring specific files, adding source files, committing changes, and mastering push/pull mechanisms to sync local and remote changes. |
| Centralized Git | Configuring SSH for secure authentication, creating remote repositories, associating local repositories with their remote counterparts, and pushing updates to maintain consistency across both. |
| Git workflow | Using pull requests, cherry-picking commits, straight merging branches, Git tags for releases, and managing post-deployment bugs and issues. |
| Branching strategies | Understanding strategies like Git workflow, feature branching, and trunk-based development to streamline collaboration and ensure effective release management. |
| Rebasing and merge conflicts | Resolving merge conflicts, using rebase to maintain linear history, and understanding when to choose merge vs rebase for a clean project history. |
| Best practices | Establishing naming conventions, commit message guidelines, and tagging standards to maintain an organized and efficient Git workflow. |
Containerization and container orchestration
The most popular containerization tools include Docker, Containerd, Rocket, CRI-O, LXC, and Podman. Likewise, orchestration platforms such as Kubernetes, Docker swarm, OpenShift, Apache Mesos, and Nomad help manage containerized applications. Docker and Kubernetes are widely adopted due to their flexibility, robust community support, and detailed documentation, making them ideal starting points for newcomers.
Essential Docker knowledge for aspiring DevOps engineers
| Topic | Details |
|---|---|
| Docker/OCI images | Understanding container image creation, tagging, and optimization for efficient deployments. |
| Docker networks | Setting up and managing container communication with bridge, overlay, and host networks. |
| Docker storage | Managing volumes and bind mounts for persistent data storage in containers. |
| Docker/OCI containers | Running, managing, and troubleshooting containers effectively. |
| Docker compose | Orchestrating multi-container applications with docker-compose files for streamlined deployments. |
Kubernetes fundamentals: must-know concepts for DevOps
| Topic | Details |
|---|---|
| Container Runtime Interface (CRI) | Understanding the container runtime that allows Kubernetes to run containers using runtimes like Containerd or CRI-O. |
| Container Network Interface (CNI) | Configuring and managing networking plugins for Kubernetes pod communication. |
| Kubernetes client and Kubernetes user | Interacting with the cluster using kubectl and understanding user roles and access controls. |
| Kubernetes metrics server | Setting up and using the metrics server for resource monitoring and autoscaling. |
| Kubernetes dashboard | Using the web-based interface for cluster management and monitoring. |
| Kubernetes resources/objects | Understanding and managing pods, deployments, services, config maps, and secrets. |
| Kubernetes storage | Configuring persistent volumes, persistent volume claims, and storage classes for data persistence. |
| Kubernetes workload | Deploying and scaling workloads using deployments, stateful sets, and jobs. |
| Kubernetes network | Managing services, ingress controllers, and network policies for pod communication. |
| Kubernetes access | Configuring RBAC (Role Based Access Control) and service accounts for secure access. |
| Kubernetes node | Monitoring and managing worker nodes in the cluster. |
| Kubernetes cluster | Setting up, scaling, and maintaining the entire cluster infrastructure. |
Together, Docker and Kubenetes form the core of modern containerization strategies, enabling efficient, scalable, and portable application deployment and management.
Continuous Integration & Continuous Delivery (CI/CD)
Continuous Integration (CI) and Continuous Delivery (CD) are critical practices in modern DevOps workflows, helping teams to automate code integration and delivery, reduce manual interventions, and improve software quality. In the context of CI/CD, Jenkins and Git play central roles in streamlining the development pipeline.
In today’s market, there are numerous CI/CD tools available, but Jenkins remains a solid and reliable framework. Despite being one of the older tools, it has strong community support and is highly versatile. Once you master Jenkins, other CI/CD tools tend to be simpler and easier to use due to its comprehensive features and wide adoption.
Essential Jenkins concepts for DevOps engineers
| Topic | Details |
|---|---|
| Introduction | Overview of Jenkins, CI/CD basics, and the benefits of implementing Jenkins. |
| Installation | Setup on various platforms, system configuration, and user management. |
| Architecture | Understanding the master-agent model, distributed builds, and scalability. |
| Pipelines and jobs | Creating freestyle jobs, working with Jenkinsfile, and understanding declarative vs scripted pipelines. |
| Plugins and integrations | Integrating essential tools like Git, Maven, notifications, and other must-have plugins. |
| Pipeline code | Writing, managing, and sharing Jenkinsfiles effectively. |
| Build management | Managing build triggers, build history, and executing parallel builds. |
| Testing | Running tests, using plugins for reporting, and visualizing test results. |
| Deployment | Automating deployment processes using Docker, Kubernetes, and cloud tools. |
| Monitoring | Generating reports, creating dashboards, and setting up alerts for build failures. |
| Scaling | Setting up agents, optimizing performance, and managing backups. |
| Security | Implementing role-based access control, securing credentials, and managing secrets. |
| Advanced | Utilizing APIs, Groovy scripting, and debugging pipelines for advanced automation. |
| Use cases | Real-world examples and use cases for Jenkins in CI/CD pipelines, including automating deployments, running tests, and integrating with version control systems like Git. |
Infrastructure as Code (IaC) tools
Infrastructure as Code (IaC) allows you to manage and provision infrastructure using machine-readable configuration files rather than physical hardware or manual processes. IaC tools automate infrastructure setup, reducing human errors and ensuring consistency across environments.
When it comes to Infrastructure as Code (IaC), several tools are widely adopted in the industry for automating and managing infrastructure. Among the most popular tools are Ansible, Chef, Puppet, and Terraform. These tools help automate the provisioning, configuration, and management of infrastructure, ensuring consistency and scalability across environments.
For those just starting out with IaC, it’s recommended to begin with Ansible and Terraform. These tools offer a great learning curve for beginners and are widely used in various environments. Ansible, known for its simplicity and agentless configuration management, allows you to define infrastructure using easy-to-understand YAML files. On the other hand, Terraform provides a declarative way to manage infrastructure and supports multiple cloud providers, making it ideal for cloud-based environments.
Starting with Ansible and Terraform will give you a solid foundation to understand how IaC works, and once you’re comfortable, you can explore other tools like Chef and Puppet for more advanced infrastructure management needs.
Ansible key concepts for new DevOps engineers
| Topic | Details |
|---|---|
| Introduction and installation | Basics of Ansible, its architecture, and setup on various platforms. |
| Inventory management | Defining hosts, groups, and dynamic/static inventory files. |
| Modules and tasks | Using core modules for system management and executing tasks. |
| Playbooks | Writing and structuring YAML-based scripts for automation. |
| Variables, facts, and templates | Managing dynamic configurations with variables, system facts, and Jinja2 templates. |
| Roles and reusability | Organizing playbooks for scalable and reusable automation. |
| Error handling and debugging | Managing task failures and troubleshooting playbooks. |
| Security | Encrypting sensitive data using Ansible vault. |
| Use cases | Configuration management, application deployment, and orchestration. |
Terraform every beginner needs to know
| Topic | Details |
|---|---|
| Architecture of Terraform | Overview of Terraform’s architecture, including how it interacts with cloud providers and manages infrastructure. |
| Terraform provider and registry | Understanding providers, configuring them to manage infrastructure, and utilizing the Terraform registry for reusable modules and resources. |
| Terraform workspace | Managing multiple environments and configurations using workspaces to isolate and manage state for different projects. |
| Terraform configuration | Writing configuration files (HCL) to define and manage infrastructure resources, variables, and outputs. |
| Terraform module | Using and creating modules for reusable, maintainable, and scalable infrastructure code. |
| Terraform plan and state | Using terraform plan to preview changes and terraform state to manage the state of infrastructure resources and apply changes. |
| Terraform Cloud | Using Terraform Cloud for collaboration, versioning, and running Terraform workflows in a cloud environment. |
Cloud services
Cloud computing has revolutionized DevOps by offering scalable, flexible, and cost-effective infrastructure that simplifies complex setups. Platforms like AWS, Azure, and Google Cloud provide pre-configured services and tools, making tasks that once required extensive manual setup as simple as plug-and-play. Whether it’s deploying servers, managing databases, or setting up CI/CD pipelines, much of the heavy lifting has been automated.
AWS and Microsoft Azure essential topics for DevOps
| Category | AWS | Azure |
|---|---|---|
| Compute | Amazon EC2, Amazon Lambda, Amazon ECS, Amazon EKS | Azure virtual machines, Azure functions, AKS, Azure app service |
| Storage | Amazon S3, Amazon EBS, Amazon Glacier |
Azure blob storage, Azure disk storage, Azure archive storage |
| Database | Amazon RDS, Amazon DynamoDB, Amazon Redshift | Azure SQL database, Azure cosmos DB, Azure synapse |
| Networking | Amazon VPC, Amazon Route 53, Amazon ELB | Azure virtual network, Azure DNS, Azure loadbalancer |
| Security and identity | AWS IAM, AWS KMS, Amazon Cognito | Azure AD, Azure key vault, Azure AD B2C |
| Monitoring and management | Amazon CloudWatch, AWS CloudTrail, AWS Config | Azure monitor, Azure log analytics, Azure security center |
| Content Delivery Network (CDN) | Amazon CloudFront | Azure CDN |
| Serverless | AWS Lambda | Azure functions |
| Container management | Amazon ECS, Amazon EKS | Azure Kubernetes Service (AKS), Azure container instances |
| Automation | AWS CloudFormation, AWS CodePipeline, AWS CodeDeploy | Azure Resource Manager (ARM), Azure DevOps, ARM templates |
Once you master the basics of a cloud platform, the next step is to explore specialized fields and services tailored to specific needs. For example, delve into AI and Machine Learning services like AWS SageMaker, Azure Machine Learning, or Google AI Platform to build intelligent applications. For video streaming, learn services like AWS Media Services, Azure Media, or Google Cloud Video Intelligence. If you’re interested in data science, focus on tools like AWS Glue, Azure Synapse Analytics, or Google BigQuery. This progression helps you expand your expertise and apply cloud technologies to solve domain-specific challenges effectively.
Observability
Observability is essential for understanding how systems behave in real-time and ensuring that applications run smoothly. It involves using tools to collect data from metrics, logs, and traces to monitor infrastructure and application performance. Observability helps DevOps engineers detect issues early, troubleshoot problems faster, and optimize system performance. Mastering observability tools is a key skill for any aspiring DevOps engineer, allowing them to manage complex systems effectively
If you’re starting with observability in DevOps, Prometheus, Grafana, and ELK stack (Elasticsearch, Logstash, Kibana) are excellent tools to begin with. Prometheus offers simple and effective time-series monitoring, while Grafana provides powerful data visualization, making it easy to interpret complex metrics. The ELK stack is widely used for managing logs and offers seamless integration for monitoring, troubleshooting, and analyzing data. These tools have strong community support, extensive documentation, and are proven in real-world production environments, making them ideal for beginners.
ELK stack: key topics for beginners
| Topic | Details |
|---|---|
| Introduction and installation | Understanding the ELK stack components (Elasticsearch, Logstash, Kibana) and setting up the stack on various platforms. |
| Data ingestion | Collecting and processing logs and data using Logstash or Beats, and transforming the data for analysis in Elasticsearch. |
| Search and indexing | Indexing data in Elasticsearch for fast search and querying capabilities, and optimizing search performance. |
| Data visualization | Using Kibana for creating dashboards, visualizing logs, metrics, and other data to monitor infrastructure health and performance. |
| Log and metrics | Aggregating logs and metrics in real-time, setting up alerting systems, and analyzing system behavior for troubleshooting. |
| Scalability and performance | Managing Elasticsearch clusters, scaling for high availability, and ensuring optimal performance of search and indexing operations. |
| Security | Securing access to Elasticsearch data, configuring user roles, enabling data encryption, and managing access control. |
| Use cases | Implementing centralized logging, application performance monitoring, security event analysis, and troubleshooting across infrastructure. |
Prometheus: key topics for beginners
| Topic | Details |
|---|---|
| Setup and architecture | Installing and configuring Prometheus for monitoring. |
| Metrics collection | Collecting data from targets and services via exporters. |
| PromQL | Writing queries to extract and analyze time-series data. |
| Alerting | Setting up alerting rules and integrating with alert manager. |
| Use cases | Monitoring system and application performance, and cloud infrastructure. |
Grafana: key topics for beginners
| Topic | Details |
|---|---|
| Setup and architecture | Installing and configuring Grafana for data visualization. |
| Data sources | Connecting to data sources like Prometheus and Elasticsearch. |
| Querying data | Writing queries to fetch and filter data for visualizations. |
| Dashboard | Creating and customizing visualizations for metrics. |
| Alerting | Configuring alerts to monitor and notify on threshold breaches. |
| Use cases | Visualizing system and application performance for insights. |
Self-learning vs training: Choosing the right path in DevOps
Deciding between self-learning and training is a critical step on your journey to landing a DevOps job in 2025. While self-learning offers flexibility and allows you to progress at your own pace, it often lacks the guidance and support of a mentor to correct your path or clarify complex concepts. Structured training, however, provides a clear roadmap, hands-on experience, and access to seasoned mentors with strong industry expertise. A great mentor can accelerate your progress by offering practical insights and real-world best practices that go beyond theory.
In the fast-evolving DevOps landscape, real-time online/offline classes are highly recommended over pre-recorded video courses. Technologies like Kubernetes, Docker, and Terraform are updated frequently, and live classes ensure you’re learning the most up-to-date practices. Real-time interaction with instructors also allows you to clarify doubts instantly and gain a deeper understanding of the tools and processes. With the right training approach, you can confidently navigate real-world scenarios, avoid common pitfalls, and stay ahead in this dynamic field. Choose the path that aligns with your learning style and career goals, but prioritize gaining hands-on expertise to secure your DevOps future.