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.
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.
Hands On Projects and Problem Solving
Discussion of practical projects and side work you have built or contributed to across domains. Candidates should be prepared to explain their role, architecture and design decisions, services and libraries chosen, alternatives considered, trade offs made, challenges encountered, debugging and troubleshooting approaches, performance optimization, testing strategies, and lessons learned. This includes independent side projects, security labs and capture the flag practice, bug bounty work, coursework projects, and other hands on exercises. Interviewers may probe for how you identified requirements, prioritized tasks, collaborated with others, measured impact, and what you would do differently in hindsight.
Date and Time Operations
Tests practical skills for working with dates and times in data, reporting, and everyday technical work. Candidates should be comfortable with date and time data types (date vs. timestamp vs. timestamp with time zone) and their storage and comparison semantics, date filtering, relative date ranges such as last-n-days or rolling windows, inclusive versus exclusive range boundaries, timezone conversions and daylight saving time edge cases, business-day and holiday-aware calculations, epoch/unix timestamp conversions, and fiscal or custom period logic. Interviewers assess the ability to translate a reporting or business requirement into correct date logic, choose the right date/time representation for a given system, and reason through common pitfalls such as timezone mismatches between systems and off-by-one boundary errors. This shows up across contexts: SQL queries, spreadsheet formulas, BI tool calculated fields and filters, and date/time handling in general-purpose code.
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.
Basic SQL Selection and Filtering
Foundational skills for retrieving and filtering data using SQL. Covers writing SELECT statements to choose columns, using WHERE clauses to filter rows with comparison operators, combining conditions with AND and OR, using NOT, pattern matching with LIKE, set membership with IN, range filters with BETWEEN, handling NULL values with IS NULL and IS NOT NULL, and basic boolean logic. Candidates should be able to write correct queries to answer simple business questions, explain why a filter returns no rows, and identify common syntax errors in simple queries.
Technology Stack and Interests
Covers both the team and product technology choices you will encounter and the candidate's own technical experience and learning interests. Topics include common frameworks and languages used in modern stacks such as React, Vue, Angular, TypeScript and backend platforms, as well as build tools, testing frameworks, deployment tooling, and styling approaches. Candidates should be prepared to explain why certain technologies were chosen, trade offs and migration paths, which parts of the stack they expect to learn on the job, and how their existing skills translate to the company stack. Interviewers also assess genuine interest in the company technologies, learning agility, adaptability to new tools, and practical experience with relevant frameworks, libraries, or patterns. Good answers combine a clear understanding of the team stack, examples of past experience, and a plan for rapid skill acquisition where needed.
Technology Selection and Framework Choices
Ability to evaluate and select appropriate technologies, frameworks, and libraries for a project, and to justify those choices with sound reasoning. Covers how to weigh project requirements, team expertise, scalability and performance needs, ecosystem maturity, community and vendor support, licensing, and long-term maintenance cost. Includes reasoning about common trade-offs (build vs. buy, established vs. emerging technology, monolithic vs. modular/pluggable tooling, open-source vs. commercial) and how to communicate a technology decision and its risks to stakeholders and teammates.
TensorFlow/PyTorch Framework Fundamentals
Practical knowledge of a major deep learning framework. Includes understanding tensors, operations, building neural network layers, constructing models, and training loops. Ability to read and modify existing code in these frameworks. Knowledge of how to work with pre-built layers and models.