Tools, Frameworks & Implementation Proficiency Topics
Practical proficiency with industry-standard tools and frameworks including project management (Jira, Azure DevOps), productivity tools (Excel, spreadsheet analysis), development tools and environments, and framework setup. Focuses on hands-on tool expertise, configuration, best practices, and optimization rather than conceptual knowledge. Complements technical categories by addressing implementation tooling.
Infrastructure as Code Tool Proficiency (Terraform/CloudFormation/Ansible)
Deep proficiency in at least one IaC tool. For Terraform: understand resources, data sources, variables, outputs, local values, modules, state management, state locking, backend configuration (S3, Terraform Cloud), and best practices (remote state, sensitive variables, module organization). For CloudFormation: understand templates (YAML/JSON), stacks, parameters, conditions, mappings, resources, outputs, and intrinsic functions. For Ansible: understand playbooks, roles, inventory, variables, handlers, and idempotency. Write reusable, maintainable code: modules for Terraform, roles for Ansible. Understand code organization, naming conventions, and team collaboration practices.
Specific Experience with Core DevOps Tools
Be prepared to discuss hands-on experience with specific tools relevant to the job description: CI/CD platforms (Jenkins, GitLab CI, GitHub Actions), containerization (Docker), orchestration (Kubernetes basics), cloud platforms (AWS, Azure, GCP), and Infrastructure as Code (Terraform, CloudFormation). Prepare concrete examples of how you used these tools.
Technical Depth and Tool Expertise
Assesses the candidate ability to demonstrate deep, practical knowledge of infrastructure tools and operational patterns rather than surface familiarity. Expect detailed conversations about infrastructure as code module design and state management, continuous integration and continuous delivery pipeline internals, container image creation and registry management, container orchestration architecture and multi tenancy, security controls and policy enforcement, testing and validation strategies for infrastructure changes, and monitoring and observability design. Candidates should be able to explain trade offs, architectural decisions, failure modes, mitigation strategies, and testing or review practices that make systems reliable at scale.
Command Line Proficiency and Troubleshooting
Comfortable using shell commands for system troubleshooting and administration. Key tools: ps (process information), top and htop (real-time monitoring), grep, sed, awk (text processing), curl (HTTP requests), netstat and ss (network statistics), du and df (disk usage), kill and killall (process termination), tar and gzip (compression). Know how to redirect I/O, pipe commands together, and create simple scripts. Understand how to use man pages.
Technology Stack Knowledge
Assess a candidate's practical and conceptual understanding of technology stacks, including major programming languages, application frameworks, databases, infrastructure, and supporting tools. Candidates should be able to explain common use cases and trade offs for languages such as Python, Java, Go, Rust, C plus plus, and JavaScript, including differences between compiled and interpreted languages, static and dynamic type systems, and performance characteristics. They should discuss application frameworks and libraries for frontend and backend development, common web stacks, service architectures such as monoliths and microservices, and application programming interfaces. Evaluate understanding of data storage options and trade offs between relational and non relational databases and the role of structured query language. Candidates should be familiar with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, infrastructure components including containerization and orchestration tools such as Docker and Kubernetes, and development workflows including version control, continuous integration and continuous delivery pipelines, testing frameworks, automation, and infrastructure as code. Assess operational concerns such as logging, monitoring and observability, deployment strategies, scalability, reliability, fault tolerance, security considerations, and common failure modes and mitigations. Interviewers may probe both awareness of specific tools and the candidate's depth of hands on experience, ability to justify technology choices by evaluating trade offs, constraints, and risk, and willingness and ability to learn and evaluate new technologies rather than claiming mastery of everything.
Docker Image Building and Optimization
Practical knowledge of creating Docker images: writing Dockerfiles, multi-stage builds, optimizing image layers for size and performance, understanding base images, managing dependencies, and best practices for production-ready images. Ability to troubleshoot image build issues and explain decisions made in Dockerfile design.
Jenkins Configuration and Declarative Pipelines
Practical understanding of Jenkins as a CI/CD tool: creating jobs, configuring build triggers, using Jenkins Pipeline (Groovy syntax), understanding declarative vs. scripted pipelines, configuring stages and parallel execution, integrating with Git repositories, managing credentials, and basic pipeline debugging and logging.
Git and Version Control
Comprehensive knowledge of Git and general version control concepts, including distributed repository fundamentals, local versus remote repositories, the staging area, commit history, and common Git commands for cloning, committing, branching, merging, pushing, and pulling. Practical skills in branching and code flow strategies such as feature branching, trunk based development, Git Flow, and GitHub Flow, including when and why teams choose each strategy, their trade offs for release cadence and team size, and how they affect continuous integration and continuous delivery pipelines. Proficiency with merge strategies and conflict resolution, rebasing versus merging, pull request and code review workflows, integrating version control with automated pipelines and code quality checks, and troubleshooting common Git issues and merge conflicts.
Containerization Fundamentals
Foundational knowledge of container technology, focused on Docker and container workflows. Topics include what containers are and how they differ from virtual machines, container images and registries, building and reading Dockerfiles, running containers, volume and file system mounting, basic container networking, image layering and size optimization, and common use cases such as reproducible deployments for machine learning and microservices. Candidates should be able to explain the container lifecycle, why containerization matters in DevOps, and demonstrate simple hands on tasks like writing a basic Dockerfile and running containers locally.