Software Engineer Interview Questions

My first step is to reproduce the issue reliably. An intermittent bug I cannot reproduce is much harder to fix than one I can trigger consistently. Once I can reproduce it, I form a hypothesis about the root cause based on the symptoms, then add targeted logging or use a debugger to test that hypothesis. I work from the outside in, starting at the point closest to the observable failure and tracing back through the call stack. I avoid changing multiple variables at once, because that makes it impossible to know what actually fixed the problem. I also keep notes as I go: writing down what I have ruled out stops me from going in circles. If I am stuck after 30 minutes I ask a colleague, because a fresh pair of eyes is almost always faster than continued solo effort.

I write tests as part of development, not as an afterthought. For any non-trivial function I write unit tests covering the happy path, edge cases, and failure modes before or alongside the implementation. I use code review as a quality gate and try to give reviewers context by keeping pull requests small and writing clear PR descriptions. I also invest time in readability: a function that is easy to understand is easier to test and easier to maintain. Beyond that, I rely on static analysis tools and linters as part of CI to catch style and logic issues before they reach review. When I find a bug in production, my first action after fixing it is to write a regression test so the same bug cannot return silently.

Early in my career I built a data pipeline that read configuration from environment variables with no validation layer. It worked fine locally and in staging, but in production a missing variable caused a silent failure that took hours to diagnose. I would now always validate and fail fast at startup: if a required config value is missing or malformed, the service should refuse to start and log a clear error rather than failing later in an unexpected way. More broadly, I have learned to invest in observability from the start. Logs and metrics added after the fact are always less useful than those designed in from the beginning. It is a relatively small upfront cost that pays back many times over during incidents.

I use Cursor as my primary editor and lean on it heavily for boilerplate, unit test generation, and straightforward refactoring. For that category of work it has cut my time significantly, and it lets me focus on the parts of a problem where I actually add value. For code review I find AI useful for catching obvious issues and suggesting edge cases I might have missed, though I always do a manual pass before anything goes to review. I am more cautious about using it for complex architectural decisions: the context window does not capture enough of the system to trust it there, and the suggestions tend to be generic rather than specific to our codebase. My honest assessment is that AI has made me faster on well-understood problems and has been less useful on genuinely novel ones. I also review everything it produces carefully because AI-generated code can be subtly wrong in ways that do not fail obvious tests.

I joined a team mid-project on a legacy monolith with minimal documentation. Rather than diving straight into the feature I had been assigned, I spent the first week reading the code around the area I would be touching, running the test suite, and mapping out the data flow manually. I also found the person who had owned that area longest and did a pair session with them to fill in context I could not get from the code alone. When I started making changes I kept them small and isolated so that each pull request was easy to review and easy to revert. The feature shipped on time and the PR reviews were smooth because reviewers could follow what I had done without needing my context.

My team decided to use a polling mechanism to synchronise state between two services. I thought an event-driven approach would be more scalable and reduce unnecessary load. I raised it in the design review with a short written comparison of both approaches, covering trade-offs in complexity, latency, and ops overhead. The team decided to proceed with polling anyway, citing the faster delivery timeline and existing team familiarity. I disagreed but I committed fully once the decision was made. Six months later we did move to events when the load issue materialised. I did not treat that as a vindication: the original call made sense given the constraints at the time.

Our local development environment required a 12-step manual setup that was undocumented and inconsistent across machines. New engineers regularly lost half a day getting it running. I spent a Friday afternoon writing a single shell script and a README that automated the entire setup process, tested it on a clean machine, and opened a pull request. It took four hours total. From then on, new hires were set up in under ten minutes. I then added it to the onboarding checklist so it would stay maintained. I did it because the friction was obvious and the fix was within my reach: it did not require permission, a meeting, or a planning cycle.

A process is an independent programme with its own memory space, file descriptors, and OS resources. Threads are units of execution that live inside a process and share its memory space. The key practical difference is isolation: a crash in one process does not automatically bring down another, whereas a crash in one thread can affect the whole process. I use multiple processes when I need strong isolation, for example when running untrusted code or separating concerns that should fail independently. I use threads when I need concurrency with shared state and the overhead of inter-process communication would be too high. In practice, for I/O-bound work I often reach for async/await patterns first, which give concurrency benefits without the complexity of managing threads explicitly.

I would start by clarifying requirements: expected write volume, read-to-write ratio, expected URL lifetime, and whether analytics are needed. For the core service, I would generate a short key by hashing or encoding a unique ID (base62 encoding of an auto-increment integer is simple and collision-free). I would store the mapping in a database with the short key as the primary key. For reads, which typically vastly outnumber writes, I would put a cache (Redis) in front of the database to serve the redirect with sub-millisecond latency. The redirect itself is an HTTP 301 (permanent) or 302 (temporary) depending on whether we want browser caching. For scale, the read path is stateless and easily horizontally scaled; the write path needs a strategy to avoid ID collisions across instances, which a centralised counter or UUID approach handles cleanly.

SQL databases store data in tables with a fixed schema and use ACID transactions to guarantee consistency. They are excellent when your data has clear relationships, you need complex queries across multiple entities, and consistency is critical. NoSQL databases trade some of those guarantees for flexibility and horizontal scalability. Document stores like MongoDB work well when your data is hierarchical and does not fit neatly into tables. Key-value stores like Redis excel at high-throughput simple lookups. Column stores like Cassandra are designed for time-series or write-heavy workloads. My default starting point is a relational database because the guarantees are well-understood and the query language is powerful. I move to NoSQL when I have a specific access pattern that a relational model handles poorly, or when I need to scale writes beyond what a single relational node can handle.