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.
Team Specific Technical Stack and Backend Systems
Discuss the team's specific technologies mentioned in the job description (Node.js, Python, Java, PostgreSQL, MongoDB, AWS, Azure, etc.). Ask about their backend architecture, how they handle scalability and reliability, deployment practices, and monitoring/alerting. Inquire about recent technical decisions or challenges they've faced. Show interest in learning their specific tech stack and systems. Ask realistic questions about the ramp-up period and learning curve.
Full Stack Project Experience Overview
Be ready to discuss 2-3 significant projects where you owned features end-to-end. Clearly articulate the frontend technologies, backend services, databases, and your specific contributions. Highlight decisions that improved performance, scalability, or user experience.
Relevant Technical Experience and Projects
Describe the hands on technical work and projects that directly relate to the role. Cover specific tools and platforms you used, such as forensic analysis tools, operating systems, networking and mobile analysis utilities, analytics and database tools, and embedded systems or microcontroller development work. For each item explain your role, the scope and scale of the work, key technical decisions, measurable outcomes or improvements, and what you learned. Include relevant certifications and training when they reinforced your technical skills. Also discuss any process improvements you drove, cross functional collaboration required, and how the project experience demonstrates readiness for the role.
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.
Backend Technology Proficiency
Assess practical, hands on experience across the backend technology stack. Candidates should be able to describe languages and server side frameworks they have used, the rationale for choosing particular database types and storage engines, how they applied caching and cache invalidation strategies, and which messaging or coordination systems they relied on. Interviewers should probe concrete examples of systems built or operated, trade offs considered between reliability latency throughput and cost, how containers and orchestration were used for deployment, and how development workflows and continuous integration and continuous delivery pipelines supported shipping and rollback.