The reported question set is broad rather than deep. It spans programming-language and lifecycle fundamentals, version control and code quality, a design question about building a system from scratch, the microservices versus monolith trade-off, debugging and poor-performance scenarios, three coding and complexity questions, and a block of behavioral prompts about refactoring, conflict, leading and prioritizing. Candidates also mention aptitude testing with quantitative reasoning among the topics, so you cannot assume every step is a conversation.
The responsibilities listed for the role are designing, coding and testing software, reviewing peers' code, and debugging existing software, in cooperation with hardware engineers, QA and product management. That points your stories and answers in a direction: a debugging answer should describe how you isolated a fault and proved the fix, a code-quality answer should mention review and tests, and a collaboration story should involve people outside your own discipline.
Because the requirements name Java, C++ or Python, choose one as your interview language and be fluent in it: its string handling, collections, how you would write the string-reversal and search-optimization answers in it, and what it costs in time and memory. The nice-to-have list adds cloud technologies, modern frameworks, data structures and algorithms, and agile practice, so keep a short, honest account of each one you have actually touched.
The reported tips are to be ready to explain your resume project by project and to practise algorithm problems on platforms such as LeetCode or HackerRank. Treat the resume walk-through as its own deliverable: for each project, know the problem, your part, the hardest bug and what you would change. Then spend the rest of your time on the four behavioral stories below, since they cover four of the reported questions between them.
Phone Screen
reportedCandidates report an initial phone screen to judge fit for the Software Engineer role. The format is not reported, so be ready to talk through code aloud, expect that a short live coding or aptitude item is possible, and ask the recruiter what to expect. Self-introduction and background questions are among the topics candidates list for the loop as a whole, so have that account ready here. Be able to name your strongest language among Java, C++ and Python, and describe one project in enough detail that follow-up questions have somewhere to go.
What to demonstrate
- Fit between your background and the Software Engineer role
- Clear spoken communication
How to prepare
- Write a spoken self-introduction that ends on the kind of work you want to do on this team, then record yourself and trim it
- Prepare two sentences on why a company making cable assemblies and connectors, with software supporting that work, interests you
- As general preparation for the whole loop, rehearse answers on your strongest programming languages, the software development lifecycle and your Git workflow, using one real project as the example
- Prepare questions to ask about the team, the work and the remaining steps, and ask what format the next stage takes
In-Person/Virtual Interviews
reportedCandidates describe one or more in-person or virtual interviews that go deeper into technical skills and behavioral attributes. Candidates are also told to be prepared to write code, so have a working setup and practise talking while you type. The reported questions are listed separately above and below as covering the loop as a whole, so prepare across all of them.
What to demonstrate
- Technical skills in more depth than the screen
- Behavioral attributes, with leadership and collaboration among the areas candidates list
- Writing code and explaining it, since candidates are told to expect to write code
How to prepare
- Write the string reversal in your chosen language three ways (two-pointer in place, the built-in, and recursion with its O(n) stack cost), state the time and space cost of each, then explain Big O in plain words
- Practise a debugging walk-through you can say in order: reproduce, narrow, form a hypothesis, test it, fix, and add a regression test
- Prepare a two-sided answer on microservices versus a monolith, including when you would start with a monolith
- Write out the four behavioral stories (refactor, difficult teammate, led a project, prioritizing) with your own actions and results, and say each one aloud
PracHub editorial advice for the preparation topics above.
Answering the question about your strongest languages with a list of names, and then stalling when asked to write or explain code in one of them.
Pick Java, C++ or Python as your interview language, and prepare its string and collection behavior, a project you built in it, and the time and space cost of the code you write.
Reciting software development lifecycle phase names, or saying 'I use Git', with no example from a real project.
Tie each phase to something you did, such as requirements, a design note, tests, review, release and a fix after release. For Git, describe your branching, pull requests and how you resolve a merge conflict.
Telling the refactoring story as 'I rewrote it and it was better', with no tests before the change and no measure afterwards.
Describe the trigger, the tests that pinned behavior, the small steps you took, and the observable result such as fewer bugs or faster changes. If you have no numbers, say what changed in practice.
Answering microservices versus monolith by declaring a winner, or debugging a performance scenario by listing random fixes.
Give both sides, tie the choice to team size, deployment needs and failure handling, and say when a monolith is the right start. For performance, describe measuring first, finding the bottleneck, changing one thing and re-measuring.
Telling the difficult-teammate or led-a-project story entirely in 'we', or blaming the other person, so your own decisions never appear.
Say what you did and why, in first person, including what you got wrong. Keep the account of the other person factual and fair.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to reverse a string. Explain your approach.
Write a function to reverse a string. Explain your approach.
Approach
- Clarify the input: mutable character array or immutable string, ASCII or Unicode, and whether to reverse in place or return a new string.
- For a mutable buffer, use two pointers, one at each end, swap and move inward until they meet. That is O(n) time and O(1) extra space. In C++, std::reverse(s.begin(), s.end()) does this on a std::string.
- Strings are immutable in Java and Python, so you build a new one in O(n) time and O(n) space. Use new StringBuilder(s).reverse().toString() in Java or s[::-1] in Python, and explain what the built-in does. Avoid repeated concatenation in a loop: in Java it can be O(n^2).
- Cover edge cases: empty string, a single character, and odd vs even length. For Unicode, reversing UTF-8 bytes corrupts multi-byte characters. StringBuilder.reverse keeps surrogate pairs together, but reversing code points still separates combining marks from their base characters (grapheme clusters).
- Mention why not recursion: it is O(n) time but uses O(n) stack depth and can overflow on long input.
Follow-up
- Reverse the order of words in a sentence in place, without reversing the letters of each word.
- How would you handle strings with combining accents or emoji correctly?
- Use the same two-pointer idea to check whether a string is a palindrome, ignoring case and punctuation.
How would you optimize a search algorithm for a large dataset?
How would you optimize a search algorithm for a large dataset?
Approach
- Ask about the data and queries first: sorted or not, static or frequently updated, in memory or on disk, exact-match or range or prefix queries, and how many queries there are per build.
- Unsorted linear scan is O(n) per query. Sorting once at O(n log n) and then binary searching at O(log n) per query pays off when there are many queries. A hash index gives O(1) average exact-match lookup but cannot answer range queries.
- For data that changes, or for range queries, use a balanced BST at O(log n) per operation, or a B-tree or B+ tree when data lives on disk. Their high fan-out keeps the tree shallow, so a lookup touches few disk pages.
- Match the structure to the query type: a trie for prefix search, an inverted index for text search, and a k-d tree or R-tree for spatial queries. A Bloom filter can skip lookups for absent keys: it has no false negatives but allows tunable false positives.
- When the data outgrows one machine, partition it by key so each query hits one shard, cache hot results, and measure. Cache locality and constant factors can make a linear scan beat a theoretically better structure on small inputs.
Follow-up
- The dataset receives thousands of inserts per second as well as queries. What changes in your choice?
- How would you support range queries on two attributes at once?
- Why does a database prefer a B+ tree to a binary search tree for an on-disk index?
Can you explain the concept of Big O notation?
Can you explain the concept of Big O notation?
Approach
- Define it: Big O describes an upper bound on how a cost grows with input size n. f(n) is O(g(n)) if there exist constants c > 0 and n0 such that f(n) <= c*g(n) for all n >= n0, so constants and lower-order terms are dropped.
- Give the common classes with an example of each: O(1) hash-table lookup on average, O(log n) binary search, O(n) linear scan, O(n log n) merge sort, O(n^2) comparing every pair with nested loops, O(2^n) enumerating all subsets.
- Separate the related ideas. Big Omega is a lower bound and Big Theta a tight bound. Best, average and worst case are different inputs, not different notations (quicksort averages O(n log n) but is O(n^2) worst case). Amortised analysis explains why appending to a dynamic array is O(1) amortised despite occasional O(n) resizes.
- Analyse space as well as time, including recursion stack depth. A recursive DFS on a path-shaped graph can use O(n) stack.
- Note the practical limits. Big O ignores constants and memory behaviour, which is why hybrid sorts such as Timsort and introsort switch to insertion sort for small runs.
Follow-up
- An inner loop runs i times for each i from 0 to n-1. What is the total complexity, and why?
- Why is quicksort's worst case O(n^2), and how do implementations avoid it in practice?
- Explain why appending to a dynamic array is amortised O(1).
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
What programming languages are you most proficient in?
What programming languages are you most proficient in?
Approach
- Rank your languages honestly and tie each to something you shipped. The requirements candidates describe list Java, C++ or Python, so lead with whichever of those you know best and name one project where you used it.
- Show depth through the language's runtime model, not just its syntax. For C++: RAII, value vs reference semantics, move semantics, and unique_ptr vs shared_ptr ownership. For Java: the JVM, garbage collection, checked vs unchecked exceptions. For Python: dynamic typing and, in standard CPython builds, the GIL preventing threads from running Python bytecode in parallel.
- Say where each language fits. C++ suits deterministic memory and latency-sensitive or hardware-adjacent code. Java suits large long-lived services. Python suits tooling, test automation and data scripts. Explain why you would pick one over another for a given task.
- Separate 'proficient' from 'have used'. Claim only what you can defend under follow-up, and say how you learn a new language quickly: the standard library, its idioms and its tooling.
Follow-up
- In C++, when would you choose unique_ptr over shared_ptr, and what problem can shared_ptr cycles cause?
- How does Java's garbage collection change the way you manage resources compared with C++ destructors?
- How would you parallelise CPU-bound work in Python given the GIL?
Can you explain the software development lifecycle?
Can you explain the software development lifecycle?
Approach
- Name the phases in order: requirements, design, implementation, testing/verification, deployment, then maintenance. For each phase, give the artefact it produces (requirements spec, design doc, code plus reviews, test reports, release, bug fixes and patches).
- Contrast the models. Waterfall runs the phases once, in sequence, and suits stable requirements. Iterative or agile (for example Scrum sprints) repeats the whole cycle in short increments and gets feedback early. The V-model pairs each requirement and design level with a matching test level.
- Explain where cost of change comes from: a defect found in requirements or design is cheaper to fix than one found after release. That is the argument for reviews, early testing and continuous integration.
- If you have worked on regulated or hardware-linked products, mention traceability: every requirement maps to a test that verifies it, and changes go through a controlled change process.
- Finish with your own experience: which model your last team used, one thing it did well, and one friction point you saw.
Follow-up
- When is a sequential, waterfall-style process the better fit than agile?
- How do you handle a requirement change that arrives mid-sprint or late in testing?
- Where does testing begin in the lifecycle you just described?
Describe a challenging technical problem you encountered and how you resolved it.
Describe a challenging technical problem you encountered and how you resolved it.
Approach
- Pick a problem that was technically hard rather than one that was only organisationally hard: a race condition, memory corruption, a performance cliff or an integration failure. State the symptom and its impact in one or two sentences.
- Walk through the diagnosis as evidence. Say what you measured or logged, which hypotheses you ruled out and how, and the step that isolated the root cause (minimal reproduction, git bisect, profiler, debugger watchpoint).
- Explain the fix and why it addresses the cause rather than the symptom. Mention any trade-off it carried, such as a lock that cost throughput or a cache that needed invalidation.
- Close with verification and prevention. Show how you proved it was fixed (a reproduction that now passes, before and after metrics) and what you added so it cannot recur (regression test, assertion, monitoring alert).
- Use 'I' for your own actions. A story that stays vague about what you personally did, or skips the root cause, comes across as a team anecdote.
Follow-up
- What was the first hypothesis you had, and what evidence ruled it out?
- If you had to solve it again, what would you check first to get there faster?
- How did you confirm the fix did not introduce a regression elsewhere?
How do you ensure code quality and maintainability?
How do you ensure code quality and maintainability?
Approach
- Layer automated checks. Unit tests cover logic, integration tests cover module boundaries, and CI runs both on every change and blocks the merge on failure. Treat coverage as a floor that exposes untested code, not as proof of correctness.
- Add static analysis and formatting so reviews focus on design: clang-tidy or sanitizers for C++, Checkstyle or SpotBugs for Java, a linter and type checker for Python, plus a formatter so style is never debated.
- Make code review concrete. Keep changes small enough to read in one pass, check naming, error handling, edge cases and test quality, and write a comment that explains the why when the code alone cannot.
- For maintainability, name the design habits: single responsibility, clear module boundaries, dependency injection to make code testable, and no duplicated logic. Refactor in small steps covered by tests.
- For legacy code without tests, write characterisation tests that pin current behaviour before changing it.
Follow-up
- What does a high coverage number fail to tell you?
- How do you resolve a disagreement with a reviewer about a design choice?
- How would you start improving a large module that has no tests?
What tools or methodologies do you prefer for version control?
What tools or methodologies do you prefer for version control?
Approach
- Name Git, which the requirements candidates describe mention, and the workflow around it: short-lived feature branches, pull requests with review, protected main branch, and CI that must pass before a merge.
- Compare branching strategies. Trunk-based development keeps branches short and integrates often, which cuts merge pain. GitFlow uses long-lived develop and release branches, which suits scheduled releases with maintained versions but adds merge overhead.
- Explain merge vs rebase. A merge preserves history with a merge commit. A rebase replays commits for a linear history but rewrites commit hashes, so never rebase a branch others have already pulled.
- Show commit hygiene and recovery tools: small focused commits with messages that say why, tags for releases, git revert to undo a pushed commit safely with a new commit, and git bisect to binary-search the commit that introduced a bug.
Follow-up
- A bad commit is already on the shared main branch. How do you undo it?
- Walk me through resolving a merge conflict in a file two people edited.
- How would you support a hotfix for an older released version while main has moved on?
How would you approach designing a new software system from scratch?
How would you approach designing a new software system from scratch?
Approach
- Start with requirements. Separate functional requirements (what users do) from non-functional ones (expected load, latency, availability, consistency, security), and estimate rough volumes so the design has numbers to answer to. For practice, try a shop-floor test-data and serial-number traceability system for cable assemblies, where data integrity and audit trails matter more than web-scale requests per second.
- Define the API and data model next: the core entities, their relationships and access patterns. Pick storage from those patterns, for example a relational database when you need transactions and joins, or a key-value store for simple high-volume lookups.
- Sketch the simplest architecture that meets the requirements, often a single well-modularised service with one database. Add caches, read replicas, queues for async work or partitioning only where the estimates show a bottleneck.
- Cover failure modes and operations: timeouts and retries with backoff, idempotent writes, backups, logging, metrics and alerts. Then cover deployment and how you would roll back.
- Close by naming the main trade-off you accepted and what you would revisit as load or team size grows.
Follow-up
- Which component fails first if traffic grows tenfold, and how would you scale it?
- What happens to your design when the database is unavailable for several minutes?
- Where would you accept eventual consistency, and where would you refuse it?
Discuss the trade-offs between using a microservices architecture versus a monolithic architecture.
Discuss the trade-offs between using a microservices architecture versus a monolithic architecture.
Approach
- Monolith strengths: one deployable unit, in-process calls with no network latency, ACID transactions across modules in one database, and simpler debugging, testing and local setup. Costs: every change redeploys everything, the whole application scales as one unit, and coupling grows without discipline.
- Microservices strengths: independent deployment and scaling per service, fault isolation, team ownership of a service, and freedom to choose technology per service. Costs: network calls that can fail partially, harder end-to-end testing, and the need for distributed tracing, service discovery and more operational tooling.
- Name the data problem. Each service owns its data, so cross-service consistency needs patterns such as sagas with compensating actions or an outbox with events, and you accept eventual consistency instead of one transaction.
- Give a decision rule: for a small team or an unclear domain, start with a modular monolith with clean internal boundaries. Extract a service when it has a distinct scaling profile, release cadence or owning team, and use a strangler-fig migration to do it gradually.
Follow-up
- How would you decide where to draw the first service boundary when splitting a monolith?
- How would you keep a work order and its test results consistent across two services without a distributed transaction?
- What would make you merge two microservices back together?
Present a hypothetical scenario where a system is performing poorly. What steps would you take to identify and
Present a hypothetical scenario where a system is performing poorly. What steps would you take to identify and resolve the issue?
Approach
- Set up a concrete scenario, for example an API whose tail latency has climbed under normal traffic. Quantify it with percentiles (p50, p95, p99) and error rates rather than averages, which hide the slow tail.
- Locate the bottleneck before changing anything. Check resource utilisation, saturation and errors for CPU, memory, disk, network and connection pools, then use tracing to see which hop of a request takes the time.
- Drill into the likely causes with specific tools: a CPU profiler or flame graph for hot code, EXPLAIN on slow queries to spot full scans, missing indexes or N+1 query patterns, lock contention, and garbage-collection pauses.
- Fix the largest contributor first. By Amdahl's law, speeding up a part that takes 10% of the time can save at most 10%. Measure before and after under the same load to confirm the gain.
- Prevent recurrence with a load test in CI or staging, alerts on the percentile that regressed, and a capacity note on the limit you found.
Follow-up
- CPU is low but latency is high. Where do you look next?
- How would you tell a slow database query apart from a slow network call in a trace?
- Your fix improved the average but not p99. What does that suggest?
How would you approach debugging a complex software issue?
How would you approach debugging a complex software issue?
Approach
- Reproduce it first, and shrink it to the smallest input and environment that still fails. Collect evidence before theorising: the exact error, stack trace, logs, the versions involved and what changed recently.
- Narrow the search systematically. Use git bisect to find the commit that introduced it in about log2(n) test runs. Disable components or swap inputs to halve the suspect area, and change one variable at a time.
- Use the right tool for the bug class: a debugger with breakpoints and watchpoints for logic errors, AddressSanitizer or Valgrind for C++ memory corruption, ThreadSanitizer or targeted logging for race conditions, and a profiler for slowness.
- Form a hypothesis that predicts something you can check, then confirm it before you write the fix. Separate the trigger (a deploy, a new input) from the underlying cause.
- Fix the root cause, add a regression test that failed before the fix, and record what you learned so the next person finds it faster.
Follow-up
- The bug appears only in production and you cannot attach a debugger. What do you do?
- How do you approach a test that fails once in every hundred runs?
- How do you prove that your fix, and not a coincidence, made the failure stop?
The plan follows the two reported stages and the question groups candidates report: fundamentals, coding, design, debugging, and behavioral stories. Each day ends with something you can reread before the interview.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Language choice and introduction
- Choose Java, C++ or Python as your interview language and write a short list of what you can do fluently in it: strings, collections, classes, error handling.
- Answer aloud: What programming languages are you most proficient in? Use one project as your evidence.
- Write and record a self-introduction and a project-by-project resume walk-through, and cut anything you cannot defend in detail.
Deliverable: A chosen interview language with a fluency list, a recorded self-introduction, and a one-page project summary (problem, your part, hardest bug).
Practice prompt ↗02Lifecycle, version control and code quality
- Answer aloud: Can you explain the software development lifecycle? Tie each phase to a step in one of your projects.
- Write a Git workflow note covering branching, pull requests, code review and how you resolve a merge conflict, then answer the version-control tools and methodologies question from it.
- Write a code-quality checklist for the maintainability question: tests, review, naming, small functions, static checks. Add one example for each item from your own code.
Deliverable: Three spoken answers (lifecycle, version control, code quality), each backed by a written outline with a real project example.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Coding and Big O
- Write the string reversal in your chosen language three ways (two-pointer in place, the built-in, and recursion with its O(n) stack cost), then state the time and space cost of each and test an empty string and a one-character string.
- Write down how you would speed up a search over a large dataset: sorted data and binary search, hashing, indexes, and what each costs in memory.
- Explain Big O out loud with examples of constant, logarithmic, linear and quadratic growth, then work two or three practice problems on LeetCode or HackerRank, saying your complexity before you run them.
Deliverable: Working, tested code for the string reversal, a one-page search-optimization note, and a Big O explanation you can say in under a page of notes.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Design and architecture trade-offs
- Outline an answer to designing a new software system from scratch: requirements, users, data, components, interfaces, failure cases, testing and rollout. Practise it on a system you know.
- Write a two-column comparison of microservices versus a monolith (deployment, testing, debugging, team boundaries, data consistency, operational cost) and add when you would start with a monolith.
- Say both answers aloud, ending each with the question you would ask before deciding.
Deliverable: A reusable design outline, a completed trade-off table, and one spoken run of each answer.
Practice prompt ↗Practice prompt ↗05Debugging and poor-performance scenarios
- Write a debugging sequence for a complex issue: reproduce, narrow down, form a hypothesis, test it, fix, add a regression test. Apply it to a bug you have fixed before.
- Build a scenario for the poor-performance question: pick a system, state the symptom, and walk through measuring first, finding the bottleneck, changing one thing and re-measuring.
- Pick your best technical story for the challenging-problem question and write it as problem, constraints, options, decision and result.
Deliverable: A debugging checklist, one worked performance scenario and a written technical-challenge story.
Practice prompt ↗Practice prompt ↗Practice prompt ↗06Behavioral stories
- Write four stories: a refactor, a difficult teammate, a project you led and a case of prioritizing several projects. Each should say what you decided, what you did and the result.
- Rewrite every 'we' as 'I' where the action was yours, then check that each story has a real number or a clear observable result.
- Say each story aloud with a limit of a few sentences per part, then record and listen for gaps.
Deliverable: Four written and rehearsed stories with results, plus a list of follow-up questions each could draw.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗07Full run-through and aptitude practice
- Run the whole loop aloud: your introduction, one coding problem, one design question, one debugging scenario and two behavioral stories.
- Practise some quantitative-reasoning questions (percentages, ratios, simple rates and sequences), because candidates list aptitude testing among the topics. Solve them under a time limit.
- Prepare five questions for the team about the software, how it works with hardware and QA, and what the next step is.
Deliverable: A recorded full mock run with a list of weak answers to fix, a page of worked quantitative-reasoning problems, and five written questions to ask.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Candidates report that the later interviews cover behavioral attributes alongside technical skills, and they list leadership among the evaluation areas, including a topic about leading operators and supervisors. As a software engineer you could answer that with a story about rolling out a tool or process to the people who run it day to day: how you gathered their needs, explained the change and handled pushback. The role is listed as working with hardware engineers, QA and product management, so choose stories that involve people outside your own discipline. Each story should show a decision you made and its result.
Can you describe a time when you had to refactor an existing system? What was your approach?
Can you describe a time when you had to refactor an existing system? What was your approach?
Approach
- Pick a refactor with a clear trigger, such as a module that was slow to change, a source of repeated bugs or duplicated logic, and name the system in one sentence. Say why refactoring beat leaving it alone and why it beat a full rewrite.
- Describe the safety net before the changes: tests that pinned the existing behavior (characterization or regression tests) and, if none existed, how you added them first. Then show small steps, such as extracting functions or classes, introducing an interface at the seam, and moving one caller at a time, each reviewed and merged on its own rather than one large branch.
- Give a result tied to the trigger: fewer defects in that area, shorter time to add a feature, faster build or run time, or fewer code paths. Use real numbers if you have them and say plainly if you do not. Do not claim improvement you did not observe.
- Name who you coordinated with: reviewers, QA, and anyone consuming the interface you were changing. Say how you kept their work from breaking.
- Finish with what you would change, for example adding tests sooner or splitting the work into smaller pull requests.
Follow-up
- How did you decide between refactoring and rewriting?
- How did you make sure behavior did not change?
- How did you persuade the team to spend time on it?
Describe a situation where you had to work with a difficult team member. How did you handle it?
Describe a situation where you had to work with a difficult team member. How did you handle it?
Approach
- Choose a conflict about a specific behavior, such as missed review turnarounds, dismissing design feedback, or unclear ownership, rather than a personality label. Describe it neutrally in two sentences.
- Show your own first move: a direct, private conversation where you asked for their view before stating yours, and found the shared goal (the feature shipping, the build staying green).
- Show the agreement you turned that into, in concrete form: a clear owner per task, a written interface or acceptance criteria, an agreed review routine. Escalating to a manager belongs after a direct attempt, and you should say when and why you did it.
- State the outcome honestly, including if the relationship stayed imperfect, and what you learned about your own part. The story sinks if you blame the person, make yourself the hero, or finish with 'I went to my manager'.
Follow-up
- What if they had not changed after that conversation?
- When do you involve a manager, and what do you bring them?
- What did you learn about your own part in the friction?
Give an example of a time you led a project. What challenges did you face?
Give an example of a time you led a project. What challenges did you face?
Approach
- Define the project in a few facts: what it delivered, how many people, how long, and your role as lead. A capstone, a team feature or a small internal tool all work, including at entry level.
- Name two real challenges, such as a scope change, a slipping dependency, a design disagreement or a teammate who was stuck. For each, give your decision, your action and the result.
- Show leadership actions rather than a tour of the project: breaking the work into tasks, assigning by strengths, setting up code review, keeping a written plan or status, unblocking people and making trade-off calls.
- Close with the outcome and one thing that slipped, including what you cut or moved and how you told stakeholders. The failure mode is describing the project in detail while your own decisions disappear into 'we'; say 'I' for your actions.
Follow-up
- What was the hardest decision you made as the lead?
- How did you handle someone who was falling behind?
- What would you change if the scope had doubled halfway through?
How do you prioritize tasks when managing multiple projects?
How do you prioritize tasks when managing multiple projects?
Approach
- State the method you actually use: list everything, then rank by deadline, impact, who is blocked by it, and effort. Separate urgent from important and re-rank when facts change.
- Illustrate it with one concrete case that had two or three competing items. Say what you did first, what you deferred, and how you told the owner of each deferred item and reached agreement.
- Cover interruptions: when a production bug or an urgent request arrives mid-task, you triage its severity, set a time limit for the investigation, and renegotiate the dates of the displaced work out loud.
- Mention the plain tools you use, such as a ticket board or a written daily plan, and how you check at the end of the day or week that the ranking still holds.
- Avoid answers that amount to working longer hours or doing whatever arrives first. Show that you can say no or not yet, and explain the cost of each deferral.
Follow-up
- What do you do when two stakeholders each say theirs is the top priority?
- An urgent bug arrives while you are mid-feature. What happens next?
- How do you handle realizing you will miss a deadline?
- 01
Can you describe a time when you had to refactor an existing system? What was your approach?
- 02
Describe a situation where you had to work with a difficult team member. How did you handle it?
- 03
Give an example of a time you led a project. What challenges did you face?
- 04
How do you prioritize tasks when managing multiple projects?
- 05
How do you handle conflicts within a project team?
- 06
Describe a time when you had to troubleshoot a significant issue in your code.
How difficult are the interviews, and how much preparation time should I expect?
Reports put the difficulty at moderate, spread across technical and behavioral questions. Candidates also report preparing for about 2-4 weeks. A week is enough to cover this guide's plan if you already code regularly. Use the extra time on algorithm practice and on rehearsing your stories aloud.
Amphenol Borisch Technologies Software Engineer candidate reports ↗How long does the process take from phone screen to offer?
Candidates report about 2-4 weeks across two stages: a phone screen, then one or more in-person or virtual interviews. Some report it running past a month, depending on how interviews and feedback are scheduled. Ask the recruiter for the next step and the format after each stage.
Amphenol Borisch Technologies Software Engineer candidate reports ↗Is remote work an option for this role?
Availability is not confirmed and reportedly varies by team. It is not guaranteed, so ask the recruiter or hiring team directly, ideally once you have a sense of the team.
Amphenol Borisch Technologies Software Engineer candidate reports ↗Which programming language should I use in the interview?
The requirements candidates describe name Java, C++ or Python. Use the one you can write most fluently under pressure, and be able to explain its string handling, collections, and the time and space cost of what you write. Do not claim a language you have not used.
Amphenol Borisch Technologies Software Engineer candidate reports ↗What technical topics should I prepare?
Candidates report questions on programming languages, the software development lifecycle, code quality, version control, designing a system from scratch, microservices versus a monolith, debugging and poor-performance scenarios, reversing a string, optimizing a search over a large dataset and Big O notation. The nice-to-have list adds cloud technologies, modern frameworks, data structures and agile practice.
Amphenol Borisch Technologies Software Engineer candidate reports ↗Is there aptitude testing?
Candidates list aptitude testing with quantitative reasoning among the topics, alongside a multi-round structure. Which stage has it is not reported, so practise percentages, ratios, rates and simple sequences, as the final day of the plan does.
Amphenol Borisch Technologies Software Engineer candidate reports ↗What should I do about the behavioral and leadership questions?
Prepare four stories before anything else: a refactor, a difficult teammate, a project you led and prioritizing several projects. Candidates report leadership as an evaluation area even for entry-level applicants, so a capstone, internship or team project can serve. Say 'I' for your own actions and finish each story with a result.
PracHub Software Engineer practice ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Amphenol Borisch Technologies Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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