Use this curated, category‑based question bank to assess fundamentals, practical judgment, collaboration, and leadership across different seniority levels. Pick 1–3 questions per section and follow up with concrete examples and trade‑offs.

General Background & Career Story

• Walk me through a project you’re proud of. What was your role and what impact did it have?
• What types of problems energize you, and which do you avoid? Why?
• Tell me about a time you disagreed with a technical decision. How did you handle it?
• Describe the trade-off you most often weigh when shipping product.
• How do you keep your skills current? Give concrete examples from the last 6–12 months.
• What’s a technical assumption you challenged recently? What happened?
• How do you evaluate if you’ve done ‘enough’ before moving on?

Programming Fundamentals

• Explain immutability. When is it useful, and when is it overkill?
• What’s the difference between concurrency and parallelism? Give examples.
• How do value types vs. reference types affect performance and APIs in your primary language?
• What’s a closure? When have you used one in a real project?
• Explain big‑O of your favorite collection operations you use daily.
• What’s the cost of exceptions versus return codes for error handling?
• Describe how garbage collection works in a runtime you’ve used (or why Rust/ARC is different).
• How do you avoid shared mutable state bugs?

Data Structures & Algorithms

• Given an unsorted stream, how would you track the top‑K elements efficiently?
• When would you pick a trie over a hash map?
• Explain the trade‑offs between quicksort, mergesort, and heapsort in practice.
• Design a rate limiter for an API (token bucket vs. leaky bucket vs. fixed window).
• How do you detect a cycle in a linked list? Explain time/space complexity.
• What’s the difference between BFS and DFS in practical use cases?
• Implement LRU cache—what operations must be O(1)?

APIs & Backend Engineering

• How do you design a versioning strategy for a public HTTP API?
• What are idempotent endpoints? Why do they matter for reliability?
• Discuss pagination approaches (cursor vs. offset) and their trade‑offs.
• How do you model and enforce request timeouts and retries safely?
• Explain how you’d design rate limiting and abuse protection for a login API.
• What contract do you promise with status codes 200, 201, 202, 204, 4xx, 5xx?
• How do you roll out a breaking API change with minimal customer impact?

Frontend Engineering

• What’s your approach to state management (local, global, server) and why?
• Explain hydration and islands architecture—when are they useful?
• How do you keep bundles small? Walk through your optimization checklist.
• What are the basics of a11y you never ship without?
• How do you prevent layout thrashing and jank?
• When do you reach for Web Workers, and what pitfalls exist?
• Explain security issues like XSS and CSRF in SPAs and how to mitigate them.

Databases & Data Modeling

• How do you choose between relational and NoSQL for a new feature?
• Explain normalization vs. denormalization trade‑offs with a concrete example.
• Describe indexing strategies for a frequently filtered, occasionally sorted table.
• What is the difference between read replicas and sharding?
• How do you handle migrations safely in zero‑downtime systems?
• Explain transaction isolation levels and a bug each level allows/prevents.
• How do you design a soft‑delete model and keep it performant?

Systems Design (Foundational)

• Design a URL shortener: what are your key components and data model?
• Design a news feed with personalized ranking—where does caching live?
• Design a real‑time chat—how do you guarantee delivery and ordering?
• How would you build a feature flag service for multiple services and clients?
• Design a metrics ingestion pipeline at 1M events/sec.
• Design a document preview service that supports many formats.

Systems Design (Advanced/Distributed)

• What consistency model would you pick for a global counter? Why?
• Explain the CAP theorem’s practical implications for a product you built.
• When do you use event sourcing or CQRS—and why not?
• How do you design backpressure across microservices?
• What’s your strategy for schema evolution for protobuf/Avro in streaming systems?
• How would you isolate ‘noisy neighbor’ effects in a multi‑tenant service?

Security & Privacy

• How do you store and rotate secrets?
• Explain the OWASP Top 10 items you’ve actually mitigated in production.
• What’s the difference between authN and authZ? How do you implement each?
• How do you protect PII while supporting analytics and debugging?
• Describe a secure file upload pipeline (validation, scanning, storage, access).
• What’s your approach to dependency risk management (SCA, SBOM, pinning)?

Testing & Quality

• What should unit tests focus on? What should they avoid?
• How do you decide between mocks, fakes, and integration tests?
• What’s a property‑based test and when is it useful?
• How do you structure test data to avoid flakiness?
• Explain your strategy for contract tests across microservices.
• How do you measure test ROI and pay down flaky tests?

DevOps, CI/CD & Observability

• Describe your CI pipeline from commit to deploy.
• How do you use feature flags and canary releases safely?
• What’s your rollback strategy when the database schema also changed?
• Which signals do you capture for observability (logs, metrics, traces) and why?
• How do you set and track SLOs/SLIs that matter to users?
• How do you debug a memory leak in a containerized service?

Performance & Reliability

• How do you approach performance profiling in your language of choice?
• What’s your cache invalidation strategy?
• How do you reduce tail latency in a distributed request?
• Explain head‑of‑line blocking and how to mitigate it.
• How would you budget errors (error budgets) for feature velocity?
• Describe a time you removed or simplified code to improve reliability.

Debugging & Incident Response

• Give a recent sev‑incident: what was the root cause and fix?
• How do you debug a production‑only bug you can’t reproduce locally?
• What information belongs in a good bug report and why?
• How do you create minimal repros efficiently?
• What’s your approach to post‑mortems and preventing recurrences?

Code Review & Collaboration

• What makes a great PR? What do you include to help reviewers?
• How do you balance speed and thoroughness in reviews?
• Describe feedback you gave that changed a teammate’s approach.
• How do you handle style disagreements on a team?
• What’s your philosophy on refactoring inside a feature PR?

Product Thinking & Ownership

• How do you translate a vague product idea into engineering tasks?
• Describe a time you pushed back on scope to hit a deadline—what was the trade‑off?
• How do you measure success post‑launch?
• Tell me about a feature you killed—how did you decide and communicate it?
• How do you incorporate user feedback and telemetry into future iterations?

Leadership & Mentorship (Senior+)

• How do you level up a junior engineer effectively?
• Give an example of driving cross‑team consensus without authority.
• How do you create and socialize technical strategy?
• Describe a time you paid down systemic tech debt. How did you sequence the work?
• What signals tell you a team is healthy? What do you do when it isn’t?

Mobile Engineering

• How do you handle offline‑first data sync and conflict resolution?
• What’s your strategy for app start‑time optimization?
• How do you manage feature flags or experiments on mobile safely?
• Explain how you structure modularization and build times at scale.
• How do you approach accessibility and dynamic type?

Data & ML Collaboration

• How do you productionize a model’s inference service for low latency?
• What’s your approach for schema evolution in data lakes/warehouses?
• How do you schedule and monitor batch pipelines (retries, idempotency, SLAs)?
• Explain shadow deployments and offline/online evaluation for ML features.

Role‑Specific: Frontend

• How do you design a component library for scale—naming, theming, API design?
• What’s your approach to cross‑browser testing and compatibility?
• Explain when to use SSR, SSG, SPA, or hybrid approaches.
• How do you safely consume third‑party scripts and SDKs?
• How do you organize CSS at scale (CSS‑in‑JS, utility‑first, BEM, etc.)?

Role‑Specific: Backend

• How do you design id generation (UUID, ULID, Snowflake) and why?
• What’s your approach to background jobs and retries (dead‑letter queues, poison pills)?
• How do you structure domain boundaries and modules to keep coupling low?
• Explain API gateway vs. service mesh and when you’d use each.
• How do you handle multi‑tenancy safely (data isolation, noisy neighbors)?

Role‑Specific: Full‑stack

• How do you keep API contracts and UI models in sync over time?
• What’s your end‑to‑end testing strategy that balances speed and coverage?
• How do you design auth flows across web and mobile consistently?
• How do you debug an issue that could be in the client, API, or database?

Behavioral (STAR)

• Tell me about a time you failed. What did you learn and what changed afterward?
• Describe a situation where you had to deliver with severe constraints.
• Give an example of influencing a decision without formal authority.
• Tell me about a conflict you resolved within your team.
• When did you have to unlearn something to succeed?

Hiring Exercises & Take‑homes

• What scope and constraints make a take‑home fair and useful?
• How do you evaluate a candidate’s submission beyond ‘does it run’?
• What rubric do you use for code clarity, tests, and trade‑offs?
• How would you design a 60–90 minute live exercise that’s realistic?

Great Follow‑ups to Deepen Any Answer

• What alternatives did you consider and why did you reject them?
• How would your approach change if [constraint X] doubled/halved?
• What debt did this create and how would you plan to pay it down?
• What telemetry would prove this worked (or didn’t)?
• If you had another week, what would you improve first?

Signals to look for: clear trade‑offs, concrete examples, measured risk, user impact, and a bias for incremental delivery with observability.