// Career guide
How to become a DevOps Engineer
Automates how software is built, tested, and deployed — owning CI/CD pipelines, cloud infrastructure, and reliability tooling.
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A DevOps Engineer automates how software is built, tested, and deployed, and helps teams run applications reliably in production. You typically own CI/CD pipelines, cloud infrastructure, configuration as code, and reliability tooling (monitoring, alerting, incident response). The role sits between development and operations: you bridge software engineering practices and systems engineering to speed delivery while reducing risk.
Successful DevOps engineers think in systems and feedback loops — version control, immutable images, automated testing, infrastructure as code, observability, and clear runbooks. You’ll spend time writing automation (scripts and code), designing infrastructure, collaborating on architecture and security, and tuning monitoring and deployment strategies to meet business SLAs.
What does the day-to-day look like?
A typical day mixes hands-on engineering with collaboration. You’ll update or create CI/CD pipelines, write Terraform/CloudFormation to provision resources, build and troubleshoot container images and Kubernetes deployments, and write scripts to automate repetitive tasks. Expect code reviews for automation, attending standups with dev teams, and planning deployment strategies (blue/green, canary, rolling updates).
You’ll also handle operational responsibilities: respond to alerts, participate in on-call rotations, run postmortems, and improve runbooks and alerting to reduce toil. Time is split between feature work (new automation, scaling), maintenance (dependency and security updates), and firefighting (incidents and degradations).
How to break in
Start with the fundamentals: Linux administration, bash/shell scripting, and basic networking concepts (TCP/IP, DNS, load balancers). Learn git and a programming or scripting language commonly used for automation (Python, Go, or Ruby). Get comfortable with containers (Docker) and container orchestration (Kubernetes) by running small apps locally and in a test cluster.
Learn one major cloud provider (AWS is the most common) and practice provisioning resources manually, then with Infrastructure as Code (IaC) like Terraform. Build end-to-end projects: create a sample web app, put it in git, build a CI pipeline (GitHub Actions/Jenkins/GitLab CI), create Docker images, deploy to Kubernetes/EKS, and expose metrics with Prometheus/Grafana. Push these projects to GitHub and document them — a portfolio of working pipelines and IaC templates is more persuasive than coursework alone.
Get practical experience through internships, SRE/ops-support rotations, or junior platform-engineer roles. Contribute to open-source tooling or internal automation, and automate something tedious in your environment to show impact. Prepare for interviews by practicing system design for deployments and reliability, debugging live systems, and explaining trade-offs (cost, complexity, recovery time). Certifications (AWS, CKAD, HashiCorp Terraform) can help visibility but won’t replace demonstrable skills and projects.
Salary expectations
In the U.S., salaries vary by region, company size, and experience. Approximate ranges: Junior/entry-level: $70,000–$100,000. Mid-level (2–5 years): $100,000–$140,000. Senior/lead/Staff: $140,000–$200,000+. Total compensation at large tech companies or in high-cost areas can be higher when stock and bonuses are included.
Job outlook
Demand for DevOps skills remains strong as organizations continue to move to cloud-native architectures and invest in automation and reliability. Roles are evolving toward platform engineering and SRE practices, so candidates who can combine software engineering with systems thinking, observability, and security will be favored.
Skills you'll need
Top courses for this career
AWS: Database
AWS: Databases Course is the fourth course of Exam Prep DVA-C02: AWS Certified Developer Associate Specialization. This course assists learners in learning the core concepts of NoSQL, contrasting it with traditional SQL databases. It covers key aspects of DynamoDB, including compute capacity, read consistency, transactions, and streams. The course also explores other NoSQL options like Amazon MemoryDB for Redis, covering cluster setup, data loading, and its comparison with ElastiCache for Redis. Finally, it provides an overview of Amazon RDS, a managed relational database service. This cours
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Building Resilient Systems
Building resilient systems requires more than knowing individual tools—it demands the ability to design architectures that anticipate failure and recover effectively. In this intermediate course, you will learn how to apply resilience engineering principles to modern distributed systems, focusing on high availability, fault tolerance, and disaster recovery planning. You will analyze how and why systems fail, identify hidden risks in system architecture, and design strategies that improve uptime and reliability. The course connects key concepts such as load balancing, redundancy, observability
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Production ML with Hugging Face
Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets. Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performan
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Mastering Ansible Automation
Learn the essentials to quickly master automation with Ansible! While this course is designed for learners with limited coding experience, it is helpful (though not required!) to have familiarity with Linux or Unix-like operating systems, the command line interface, and the basics of shell scripting. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant fee
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Certified Kubernetes Administrator (CKA): Unit 6
This course helps you assess your readiness and build practical skills before taking the real exam. You’ll get step-by-step instructions on using the sample exams and grading scripts, so you can make the most of your practice. The course includes two full-length practice exams that closely match the actual certification test. Practice solving real-world tasks under timed conditions, then review your answers with detailed instructor walkthroughs. This process will show you what you know and where you need to improve, so you can focus your study time effectively.
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Lakehouse Architecture for AI-Native Data Platforms
This course focuses on designing governed, scalable lakehouse architectures that support AI-native data platforms. Learners translate AI workload requirements into data product SLOs, compare open table formats, design ingestion and replay strategies, manage schema evolution, support reproducibility, and define observability signals for freshness, latency, throughput, and cost. The course emphasizes architecture and operational patterns rather than vendor-specific platform administration. By the end of the course, learners can explain when a lakehouse is preferable to a warehouse or data lake f
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Configuring your IDE for absolute beginners with AWS Cloud9
So by the end of this course, you will learn to set up an online IDE. For this, you will use the AWS cloud9 environment. You will learn to configure the AWS cloud 9 environment, you will explore the IDE, you will write some python programs, you will learn to create files and folders manually and through the command line . You will also learn to power off the instance and learn to delete the environment as well. Before starting this project, you must have an AWS account.
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Linux Fundamentals
This course is the first of a series that aims to prepare you for a role working as an information technology professional. In this course, you will be introduced to the Linux operating system. You will learn about the licensing model and several Linux distributions. We will go through some services delivered in the Linux operating system. We will conclude this course by looking at how to search and analyze text using command line tools in the operating system.
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Advanced CloudStack Architecture & Scalability
Scaling a CloudStack environment starts with building automation that can provision, validate, and evolve infrastructure reliably as demand grows. In this advanced, hands-on course, you will learn how to move beyond manual setup by automating a full CloudStack zone using infrastructure as code. You will begin by provisioning core components with Ansible or Terraform, focusing on modular design and repeatable deployments. As you progress, you will add CI checks that help validate changes and prepare automation for staging and production use. You will then extend the environment by introducing a
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Frequently asked questions
How is DevOps different from Site Reliability Engineering (SRE)?
DevOps is a cultural and practice-oriented approach that focuses on collaboration and automation between dev and ops. SRE applies software engineering principles specifically to operations and reliability, with formal objectives like SLOs and error budgets. In practice there’s overlap: SRE roles emphasize measurable reliability and often require more on-call/incident work, while DevOps roles can span CI/CD, tooling, and platform automation.
Do I need a computer science degree to become a DevOps engineer?
No. Employers value demonstrated skills (automation, cloud, IaC, Kubernetes) and practical experience over a specific degree. A CS degree can help with fundamentals, but you can break in with projects, certifications, internships, and hands-on practice.
What should I put in my portfolio or resume?
Show complete, reproducible projects: git repos with CI pipelines, Terraform modules, Kubernetes manifests or Helm charts, Dockerfiles, and monitoring dashboards. Include short READMEs that explain architecture, deployment steps, and what you automated. Highlight metrics where possible (reduced deployment time, decreased incident count, cost savings).
Which tools should I learn first?
Start with Linux, git, Docker, a CI system (GitHub Actions or Jenkins), and one cloud provider (AWS). Then add Terraform for IaC, Kubernetes for orchestration, and a monitoring stack (Prometheus/Grafana or Datadog). Shell scripting and one general-purpose language (Python or Go) are also high priority.
Are certifications worth it?
Certifications (AWS, CKAD, HashiCorp Certified: Terraform Associate) can help get past recruiters and show commitment, but they won’t replace hands-on experience. If you have limited resume experience, a targeted certification plus portfolio projects is a reasonable strategy.
How much on-call and incident response is involved?
Many DevOps roles include on-call rotations and incident response, especially in smaller teams. The amount varies: platform or SRE teams usually have more structured on-call duties with runbooks and alerting, while pure CI/CD-focused roles may have less. Ask about on-call expectations in interviews.