As a Software Engineer at Linxens, you are at the intersection of high-precision manufacturing and digital connectivity. Linxens is a global leader in the design and manufacture of components for security and identification, and your role is fundamental to ensuring that our software systems support the complex, automated environments required for such high-stakes production. You will be tasked with building, maintaining, and optimizing the systems that drive our operational excellence. This role is critical to the business because it bridges the gap between raw hardware capabilities and the software logic that governs our production lines. You will not only be writing code but also engaging with the physical realities of the shop floor. Whether you are working on process design, equipment maintenance, or quality assurance, your work directly impacts the reliability and throughput of our global manufacturing footprint. You will find this role uniquely challenging, as it requires both deep technical proficiency and the ability to collaborate across multidisciplinary teams in a fast-paced industrial setting.
Preparation focus
editorialNo round sequence has been reported for this company, so confirm the format with your recruiter and work the reported questions below.
What to demonstrate
- Breadth across the topics this company reports testing
- Whether you confirm the format before preparing for it
How to prepare
- Ask the recruiter for the sequence, the duration of each stage and whether you will be writing code
- Work the reported questions below and time yourself
PracHub editorial advice for the preparation topics above.
Prepare your stories
Use the STAR method (Situation, Task, Action, Result) to structure your behavioral answers, ensuring you highlight your personal contribution.
Research our products
Understanding our core business—smart cards, security, and identification components—will help you frame your technical answers more effectively.
Be ready for technical depth
Even in behavioral rounds, interviewers may dig into the technical details of your past projects. Know your resume inside and out.
Showcase your adaptability
Emphasize instances where you learned a new tool or technology on the fly to solve a pressing problem.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Identify the heaviest tenants in a five-minute window under memory pressure
The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.
Approach
- Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
- Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
- State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
- Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.
Follow-up
- The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
- Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
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?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
How do you approach the integration of software with physical hardware or equipment?
How do you approach the integration of software with physical hardware or equipment?
Approach
- Start from the interface contract, not the code: find out what the equipment actually exposes (vendor SDK, serial protocol, Modbus register map, OPC UA nodes, discrete I/O), its timing limits, and which side owns safety. Read the datasheet or interface spec before writing anything.
- Put the device behind an adapter layer with a small interface such as
connect(),read_status()andsend_command(), so business logic never handles raw bytes or registers. This is what lets you plug in a simulator for tests and swap a vendor later without touching the rest of the system. - Model the machine as an explicit state machine (idle, running, fault, e-stop) and allow each command only in the states where it is valid. Give every request a timeout and validate every response: framing, checksum or CRC, and a sequence or transaction id where the protocol has one.
- Treat TCP and serial as byte streams: one read can return half a frame or two, so buffer and reassemble. Reconnect with backoff, and after a timeout read the device state back before resending; blindly retrying a non-idempotent action like a move or dispense can actuate twice.
- Keep hard real-time control in the PLC or firmware, and safety functions (e-stop, guard interlocks) in safety-rated hardware such as safety relays or a safety PLC. PC software supervises, sets parameters and logs data; a weak answer makes the PC app what keeps people and tooling safe.
- Bring it up incrementally: log every command and response with timestamps, run against an emulator first, then connect read-only, then send single commands at low speed with an engineer present, and only then run full cycles.
Follow-up
- What if the vendor SDK is a 32-bit Windows-only DLL? Host it in a separate small process behind an IPC or local HTTP/gRPC API, so the main app stays 64-bit and a crash inside the DLL cannot take it down.
- Polling or events? Poll when the device offers no push mechanism or you need a fixed sample rate; prefer subscriptions (OPC UA monitored items, MQTT) for lower latency and load, but keep a heartbeat to catch silent disconnects.
- How do you handle clock differences between device and host? Choose one authoritative clock or sync via NTP/PTP, and store both the device and host timestamps when event ordering matters.
How do you ensure your code is maintainable and scalable within a production environment?
How do you ensure your code is maintainable and scalable within a production environment?
Approach
- Define both terms first: maintainability is how cheaply and safely someone else can change the code; scalability is handling growth in load, data volume, or number of machines and sites without a redesign. They conflict mainly when you scale prematurely; clean module boundaries serve both.
- For maintainability, keep modules small and cohesive and separate domain logic from I/O (database, network, devices) behind interfaces, so the logic is testable in isolation. Enforce style with a formatter and linter in CI, keep configuration out of code, and use code review to spread knowledge.
- Make automated tests the safety net that allows change: fast unit tests on pure logic, integration tests against a real database in a container, and contract tests on APIs between services. Treat coverage as a signal, not a target; the goal is refactoring without fear.
- For scalability, profile and load-test first to find the real bottleneck. Keep services stateless, with session and job state in a shared database or cache, so they scale horizontally; buffer bursty work through a queue; batch writes or add indexes where the numbers show a hot path.
- Make it operable in production: structured logs with correlation ids, metrics on latency, error rate and queue depth, health checks and alerts. Ship behind feature flags, version APIs, and use expand/contract schema migrations so deploys are backward compatible and can roll back.
- A weak answer recites "clean code and SOLID" with no evidence. Anchor yours in one example, e.g. turning a script hard-coded for one machine into a config-driven service with tests, and state what it changed (time to add a new machine, incidents per month).
Follow-up
- How do you reduce technical debt without stopping feature work? Keep it tracked and visible, refactor in small steps next to related feature changes, and reserve a fixed share of each cycle for larger items.
- When is designing for scale the wrong call? When load is known and small; build the simplest thing, measure it, and keep clean boundaries so only the hot path needs rework later.
- How do you change a schema used by a live service? Expand/contract: add the new nullable column, dual-write, backfill, switch reads, ship a release that stops writing the old column, then drop it once no deployed or rollback version uses it.
What is your process for testing software before it is deployed to live manufacturing equipment?
What is your process for testing software before it is deployed to live manufacturing equipment?
Approach
- Frame the risk first: a bug on live equipment can damage tooling, scrap product or hurt someone, and rolling back mid-batch is not free. So testing is layered, each layer closer to the real machine, and hardware safety interlocks stay in place however good the software tests are.
- Unit-test the control logic with I/O stubbed out: recipe and parameter validation (units, min/max setpoints, invalid combinations) and every state-machine transition, especially fault, abort and e-stop recovery paths, which rarely happen on the floor and so are rarely exercised.
- Run the software against a simulator or emulator of the equipment in CI. Replay recorded real traffic and inject faults on purpose: timeouts, malformed frames, a disconnect mid-cycle, sensor values out of range. Check the software ends in a safe, known state each time.
- Move to hardware-in-the-loop: the real controller (PLC or embedded board) on a bench, wired to an I/O simulator of the machine. Then use a spare machine or staging line: dry runs with no or scrap material, cycle-time checks, and a soak test to catch leaks, reconnect bugs and drift.
- Gate the release: sign-off with process or equipment engineers, deploy in a planned downtime window, pilot on one machine or line first, and judge it against agreed acceptance criteria (cycle time, yield, alarm rate). Keep the previous version and its configuration ready to restore as a pair.
- Keep traceability: version code and recipes together, and record the software version with each production record, so any quality issue can be traced to a specific release.
Follow-up
- How do you test a fault that is dangerous to trigger on real hardware? Inject it in the simulator or at the adapter boundary; on the bench, use a safe proxy such as unplugging a sensor with the machine already in a safe state.
- What if there is no spare machine for staging? Simulate, then run it read-only beside the live system and compare decisions, if the device allows a second client (a Modbus RTU bus has one master); else replay recorded traffic.
- What are your acceptance criteria for the pilot? Pick measurable ones up front: cycle time within a set tolerance, no new alarm types, yield unchanged over an agreed number of units or shifts.
How would you optimize an existing software process to improve efficiency or reduce manual intervention?
How would you optimize an existing software process to improve efficiency or reduce manual intervention?
Approach
- Interpret it as an existing workflow that is slow or needs people to keep it running (manual data entry, copying files, restarting a stuck job). Start by measuring: time each step, and count how often and why humans intervene from logs, tickets or by watching operators.
- Rank candidates by cost today, frequency × (minutes per occurrence + error rate × minutes to fix an error), weighed against the effort to change it. For runtime, Amdahl's law caps the gain: speeding up a step that is 5% of the total saves at most 5%, so target the dominant step.
- Remove causes before automating symptoms: if operators restart a job because it hangs, fix the hang rather than scripting the restart. Automate routine steps fully; for judgment calls, give people better alerts and tools instead of forcing full automation.
- For raw speed, profile first, then apply the usual wins: batch database writes and round trips, remove N+1 queries, parallelize independent I/O, process incrementally instead of reprocessing everything, and cache with explicit invalidation. Re-run the same benchmark to prove each gain.
- Build the automation to fail safely: make steps idempotent so retries are harmless, validate inputs, alert and escalate to a person on failure, keep an audit log and a manual override. Run it in parallel with the manual process and compare outputs before switching over.
- Report against the baseline you measured: cycle time, manual touches per day, error rate, hours saved per week. Monitor the automation itself, because an automated step that fails silently is worse than the manual one it replaced.
Follow-up
- How do you handle an edge case the automation cannot classify? Route it to a manual queue with its context attached, alert an owner, and count how often each case occurs so the frequent ones get automated next.
- What if the fix needs changes in a system you do not own? Bring data on the cost of the manual step to its owners and propose a contract change; meanwhile automate your side behind an adapter.
- When is automation not worth it? When building and maintaining it costs more than the time and errors it removes over its life; rare, judgment-heavy tasks often merit only a checklist or a helper script.
Edge instances grow 400 MB per hour until the nightly restart
Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.
Approach
- Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
- Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
- Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
- The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.
Follow-up
- Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
- How would you have detected this before an OOM, given the nightly restart masked the trend?
Built from the topics and questions Linxens candidates report; no round sequence has been reported.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Establish the Linxens format
- No round sequence has been reported, so ask your recruiter for the sequence, the duration of each stage and whether you will write code.
Deliverable: A written reply from your recruiter confirming the format.
02Work Software Engineering
- Spend the session on Software Engineering, which Linxens candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
03Work Quality Engineering
- Spend the session on Quality Engineering, which Linxens candidates report being tested on.
- Write one worked example in Quality Engineering and time yourself on it.
Deliverable: One timed worked example in Quality Engineering.
04Work Maintenance Engineering
- Spend the session on Maintenance Engineering, which Linxens candidates report being tested on.
- Write one worked example in Maintenance Engineering and time yourself on it.
Deliverable: One timed worked example in Maintenance Engineering.
05Answer out loud: Technical & Domain Expertise
- Answer aloud, timed: How do you approach the integration of software with physical hardware or equipment?
- Answer aloud, timed: Can you describe your experience with troubleshooting complex software-to-hardware interfaces?
Deliverable: Spoken answers to 2 reported Technical & Domain Expertise question(s), under time.
06Rehearse your own examples
- Prepare three examples from your own work where you made the decision, each with the outcome you can quantify.
Deliverable: Three examples written out, each with a number attached.
07Dry run for Linxens
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Can you describe your experience with troubleshooting complex software-to-hardware interfaces?
Can you describe your experience with troubleshooting complex software-to-hardware interfaces?
Approach
- A strong answer shows two things: that you have debugged across the software/hardware boundary, not only inside application code, and that you isolated the faulty side with experiments instead of guessing or blaming the other team.
- Pick one specific, non-obvious incident at the boundary, e.g. intermittent timeouts traced to missing RS-485 termination, wrong readings traced to 32-bit register word order, or a firmware mismatch. Avoid stories that end in "we restarted it" with no root cause found.
- Open with the symptom and its cost: what failed, how often, and what it stopped (a station down, scrapped parts, operators re-entering data). Intermittency matters, so say whether you could reproduce it and how long that took.
- Spend most of the answer on the isolation: how you split the problem by holding one side constant (loopback test, known-good device or simulator, swapped cable or port) and which tools gave the evidence (serial or Wireshark capture, logic analyzer, device logs correlated by timestamp).
- Close with the fix and the lasting change: the code or configuration fix, plus the guardrail you added so it cannot recur silently, such as raw-frame logging, a startup firmware-version check, stricter timeout handling, or a regression test against the simulator.
- Quantify before and after, e.g. "timeouts went from about three per shift to zero over a month", plus downtime saved or time to diagnose. If your background lacks factory equipment, use the closest real boundary (USB or serial peripheral, embedded board, IoT sensor, printer) and say so plainly.
Follow-up
- What if the root cause was in vendor firmware you could not change? Describe the workaround you shipped on your side, such as a slower poll rate or longer timeouts, and how you tracked and verified the vendor's eventual fix.
- What would you do differently next time? Name a concrete signal you lacked at the start, like raw-frame logs or error counters, and the monitoring you added afterwards.
- How did you work with the vendor or the electrical team? Show you brought evidence such as captures and timestamps rather than blame, and explain how the fix was agreed and verified.
Reverse your own decision and price the reversal
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
Approach
- State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
- Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
- Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
- Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.
Follow-up
- What in that decision was irreversible, and did you know it was irreversible when you made it?
- How did you tell the people who had already built on top of the original decision?
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
- 01
Can you describe your experience with troubleshooting complex software-to-hardware interfaces?
- 02
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
- 03
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
How long does the hiring process usually take?
While it can vary by region, we aim for a swift process. Candidates have reported receiving offers as quickly as one day after the final interview.
Linxens Software Engineer candidate reports ↗Is the interview process difficult?
The difficulty is typically described as average, focusing on your ability to apply your knowledge to our specific context rather than abstract brainteasers.
Linxens Software Engineer candidate reports ↗What is the culture like at Linxens?
We are a professional, global organization that values directness and technical expertise. We appreciate candidates who are proactive and genuinely interested in the industrial applications of their work.
Linxens Software Engineer candidate reports ↗How much on-site work is required?
Given the nature of our products and manufacturing processes, this role often requires a physical presence to interface with equipment and teams on the shop floor.
Linxens Software Engineer candidate reports ↗What topics does Linxens test in interviews?
Linxens interviews most often cover Stakeholder Management, Process Improvement, Problem Solving, Documentation & Reporting, and Software Engineering. The exact emphasis depends on the specific role you apply for.
Linxens Software Engineer candidate reports ↗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.
- 01Linxens 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
Explore related engineering environments and compare the preparation focus.
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