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Practical capability groupings across engineering, communication, systems, and operations.
API contracts, request and response boundaries, versioning, compatibility, and operational behavior.
API request handling, routing, contracts, validation, error handling, and production behavior.
Keeping cached data fresh through TTLs, delete-on-write, versioned keys, event-driven invalidation, and stale-data controls.
Cloud runtime, configuration, scale, network boundaries, deployment model, and production readiness decisions.
Practical coding interview exercises with constraints, examples, edge cases, and implementation reasoning.
Working with teammates through review, disagreement, mentoring, and shared ownership.
Using arrays, lists, dictionaries, sets, grouping, ordering, and DTO shaping effectively.
Database access patterns, query shape, ORM behavior, transactions, and data-bound API performance.
Scoping, sequencing, estimating, and making delivery decisions under constraints.
Shared cache design, Redis usage, cache-aside, session state, rate limiting, and cache failure behavior across app instances.
Explaining technical work, risk, trade-offs, and decisions clearly to engineers and stakeholders.
Choosing pragmatic technical paths with clear trade-offs, risk control, and long-term thinking.
UI state, forms, rendering, accessibility, data fetching, and frontend architecture.
Shipping a product feature across UI, API, backend behavior, data state, and production readiness.
Communicating status, impact, mitigation, and follow-up during production issues.
Core language behavior and concepts that affect day-to-day engineering decisions.
Learning unfamiliar technology, adapting from feedback, and improving team process.
Async tasks, thread pool behavior, synchronization, cancellation, and race-condition risks in .NET.
.NET memory behavior, allocation control, leak prevention, and production diagnostics.
Logging, metrics, tracing, correlation IDs, and operational visibility for APIs.
Taking responsibility for outcomes, delivery, quality, follow-up, and operational behavior.
Parallel workloads, concurrency limits, CPU-bound work, and throughput trade-offs.
Finding and fixing latency, throughput, dependency, database, serialization, cache, and resource bottlenecks in backend systems.
Breaking down a task, choosing data structures, reasoning through edge cases, and explaining trade-offs.
Investigating slow APIs, failed requests, logging gaps, dependency failures, and operational incidents.
Handling production risk, incidents, bugs, mitigation, and prevention.
Deployment safety, environment promotion, rollback, feature flags, release traceability, and production change control.
Resilient API behavior, retries, timeouts, idempotency, rate limits, and failure handling.
Authentication, authorization, token handling, API protection, and secure request boundaries.
Cohesive code, dependency management, behavioral contracts, application boundaries, and proportionate design decisions.
Unit, integration, end-to-end, smoke, and regression testing strategy.