Software Engineering Practices Topics
Covers industry-standard practices for building maintainable, high-quality software, including code quality, maintainability, documentation, and effective technical communication within engineering teams.
Code Quality and Engineering Practices
Addresses practices for maintaining and improving code quality while delivering features. Topics include code review standards, testing strategies such as unit testing, integration testing and end to end testing, test automation, continuous integration and continuous delivery, static analysis and linting, refactoring practices, and technical debt management. Also covers how to balance shipping speed with long term maintainability, how to measure quality and when to prioritize debt repayment versus new work, and how to communicate quality tradeoffs to nontechnical stakeholders.
Technical Excellence and Engineering Practices
Practices and cultural habits that maintain high engineering standards across teams. Topics include establishing and enforcing code review standards, testing strategies, continuous integration and delivery practices, documentation norms, knowledge sharing, learning culture, and measurable engineering health metrics. Also includes approaches to mentor engineers, build technical competency across the team, and structure learning programs that raise the whole organization.
Codebase Architecture and Modularity
Designing and organizing large codebases to be modular, maintainable, and scalable. Candidates should be able to describe how to identify component boundaries, define stable interfaces and contracts, select appropriate service boundaries versus library modules, structure packages and ownership, and enable multiple teams to build and release independently. Discussions should cover dependency management, strategies to reduce coupling, versioning and backward compatibility, testing and continuous integration practices, incremental refactoring approaches, and how to balance short term delivery with long term maintainability and technical debt reduction.
Delivery Velocity and Quality
Balancing rapid feature delivery with engineering quality and reliability. Topics include release processes, automated testing strategies, code review culture, feature flagging and progressive rollout techniques, deployment strategies such as canary and blue green, monitoring for regressions, technical debt trade offs, prioritization frameworks for scheduling work, and stakeholder communication when making release trade offs. Interviewers will look for concrete examples of delivering under time pressure while preserving system health and long term sustainability.
Engineering Culture and Quality Standards
Assesses how candidates build and sustain an engineering culture that drives high quality and reliable delivery without unduly slowing velocity. Candidates should discuss code review standards, testing strategies including unit testing integration testing and end to end testing, test automation, continuous integration and continuous delivery pipelines, deployment and release practices such as feature flagging and canary releases, observability and monitoring, incident response and blameless postmortems, service level objectives and error budget thinking, metrics and feedback loops, developer experience and tooling, and approaches to embed quality through hiring coaching and process. The topic also covers balancing technical debt pay down with feature delivery and evolving quality standards as the organization grows.
Technical Problem Solving
Assess the candidate's structured approach to ambiguous and unfamiliar technical problems. This covers how the candidate clarifies requirements, identifies unknowns and key assumptions, breaks problems into testable components, forms hypotheses and designs experiments, and uses instrumentation and measurement to validate conclusions. Interviewers will probe profiling and benchmarking techniques, creation of lightweight prototypes or proofs of concept to reduce risk, root cause analysis methods, and how data and metrics drive trade off decisions. Also evaluate communication of findings, iteration based on evidence, and pragmatic decision making when balancing time, quality, and resource constraints.