A Software Engineer at SciCan plays a pivotal role in bridging the gap between sophisticated medical hardware and the software systems that control, monitor, and optimize them. As a leader in infection control and sterilization technology, SciCan relies on engineers who can translate complex clinical requirements into reliable, high-performance code. Your work directly impacts the safety and efficiency of healthcare facilities worldwide, ensuring that medical equipment operates with precision and adherence to strict industry standards.
This role is inherently multidisciplinary, requiring you to work at the intersection of embedded systems and application-level software. You will be responsible for creating robust solutions that power life-critical devices, meaning your code must be as stable as it is innovative. Whether you are improving existing device interfaces or architecting new control logic, you will be a key contributor to the technical roadmap that keeps SciCan at the forefront of medical technology.
Because SciCan operates in the regulated medical device space, your interviewers will be looking for a mindset that prioritizes safety, documentation, and reliability above all else.
Preparation focus
editorialNo round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.
What to demonstrate
- Breadth across SQL, experimentation and product reasoning
- Ability to state assumptions before choosing a method
How to prepare
- Drill the practice exercises below and time yourself
- Prepare three quantified stories about decisions you drove
PracHub editorial advice for the preparation topics above.
Treating a medical record number or a member ID as a globally unique key and joining on it directly.
These identifiers are unique only within the authority that issued them. Two facilities in one network routinely have the same medical record number for different people, and member IDs get reissued when someone changes plans. Joining on the bare value merges two patients' records, which is the most damaging failure available in this domain, and it passes every test written against a single-facility fixture because the collision only appears once a second source is connected.
Upserting on the order or result identifier, so a correction overwrites the original row.
It makes the question 'what did the clinician see at 14:02' unanswerable, which is exactly what an incident review or a legal hold asks. It also leaves downstream consumers that already acted on the preliminary value with no correction event to react to, because the state transition was collapsed into a single mutated row and never emitted.
A queue or buffer with no bound
Every producer-consumer boundary needs a capacity and a policy for reaching it: block the producer, shed load, or drop the oldest entry. Unbounded buffering converts a temporary slowdown into memory exhaustion and hides the backpressure signal that would have revealed the consumer was falling behind.
Comparing floating-point values for equality, or holding money in them
Binary floating point cannot represent 0.1 exactly, so repeated addition drifts and an equality check fails on values that are mathematically equal. Store currency as integer minor units or a decimal type, and compare floats against a tolerance you chose for a stated reason.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Merge twelve resource streams into one patient summary page
A patient summary fans out to between 8 and 12 resource types. Each returns a network-backed, paged iterator of resource versions sorted by issued_ts descending, up to 200,000 versions per type for one person. Return the 50 most recent current versions across all types, where current means no later version supersedes it within the same logical resource, and a logical resource whose current version is entered_in_error is omitted entirely. You may not materialise the iterators. Give time and space in terms of k types, the result size and the page size.
Approach
- k-way merge with a max-heap holding one head per iterator, keyed on issued_ts. Seeding is O(k), each pop is O(log k), so reaching R emitted rows costs O(k + P log k) for P pops, with O(k) heap space plus one page buffered per iterator. Fetching everything and sorting is O(V log V) over V up to 2.4 million versions and drags every page across the network to produce 50 rows.
- Suppress with a hash set of logical resource ids already seen, recorded on first sight whether or not that version is emitted. A logical resource has exactly one resource type, so all of its versions arrive on one iterator, and that iterator is descending in issued_ts: the first version you see for a logical resource is its newest. Deciding on first sight and suppressing every later pop for that id is therefore correct in one pass with no lookahead.
- Count emits, not pops. A correction-heavy chart can burn many pops per emitted row, so a loop that stops at 50 pops returns a short page. Put a bound on total pops as well, and when it trips, return what you have with a continuation token rather than spinning.
- Break issued_ts ties deterministically on (resource type, resource id). Without it two identical requests return two different orderings and the next page silently skips or repeats rows.
- Handle entered_in_error at first sight: the erroneous version still supersedes its predecessor, so record the id in the seen set and emit nothing, dropping the whole logical resource instead of falling back to the value it replaced. Recording it is the load-bearing half. Skip it and the next pop re-displays the value a clinician already retracted.
- Summarise the budget honestly: the composite p99 is what the user feels, and it is bounded below by the slowest of the k iterators, so the merge fixes the ordering cost but not the fan-out tail.
Follow-up
- One of the twelve iterators has a p99 of 400ms while the rest return in 20ms. What is your composite p99 and what would you change first?
- The user pages to rows 51 through 100. How do you resume without re-reading from the top, and what breaks if issued_ts is not unique?
- One resource type is accidentally returning ascending order. How would your code detect that rather than quietly emitting the oldest rows?
Flatten overlapping coverage spans into a primary-payer timeline
For one enterprise_person_id you hold up to 10,000 coverage_span rows: coverage_id, payer_id, plan_id, effective_date, termination_date (null means open-ended), coverage_order (1 is primary), status, valid_from and valid_to (system time). Given a system instant T and a date range D0 to D1, return disjoint business-date intervals covering that range, each labelled with the active coverage_id of lowest coverage_order, and with uncovered stretches returned explicitly. termination_date is the last covered day. Only status active counts. Target O(N log N) time, O(N) space.
Approach
- Apply the system-time slice before any interval logic: keep rows where valid_from is at or before T and valid_to is after T. That single predicate is what makes the answer 'what we believed at T' rather than 'what we believe now', and a retroactive termination loaded after T leaks straight into the output if you skip it.
- Convert each surviving row to a half-open day interval from effective_date up to termination_date plus one day, using positive infinity for a null termination. Half-open removes every off-by-one at the join between a termination and the next plan's effective date, which is where same-day switches get turned into false gaps.
- Sweep: emit 2N boundary events, sort by date in O(N log N), and hold the active set in a min-heap keyed by (coverage_order, coverage_id) with lazy deletion, popping the top while it has already expired at the current boundary. Each coverage is pushed once and popped once, so the sweep is O(N log N) total and O(N) space.
- Emit an output interval whenever the heap top changes between consecutive boundaries, and emit an explicit uncovered interval when the heap empties. A gap reported as a gap is a different answer from a gap omitted, and downstream the difference is a denial versus a silent assumption of coverage.
- Fix the tie-break and state it: lowest coverage_order, then lowest coverage_id. Two rows at order 1 is a data error, and without a deterministic rule the coordination-of-benefits answer changes between two runs over identical data.
- Clip to D0 and D1 last, so a coverage that starts before D0 still contributes its correct order inside the window.
Worked solution 30 min
- Filter to rows live at T, then map each to a half-open interval and a (coverage_order, coverage_id) priority.
- Build the boundary list, sort it, and sweep with a lazily-deleted min-heap.
- At each boundary, drop expired heap tops and compare the new top to the previous one, emitting an interval on change.
- Emit an uncovered interval whenever the heap is empty between two boundaries.
- Clip the emitted list to D0 through D1 and assert the intervals are disjoint and contiguous.
Follow-up
- Run the same query at two system instants and diff the timelines. What did the correction change, and what does each extra instant cost you?
- A termination arrives with an effective date earlier than the service date of a claim that already adjudicated and paid. What does the timeline now say, and what has to happen to that claim?
- Two enrolment files disagree on coverage_order for a dependent. What does your tie-break do, and what should the system do instead of tie-breaking?
Resolve enterprise identity from a link log that supports unmerge
person_identity_link holds link_id, enterprise_person_id, assigning_authority, source_person_id, match_score, link_status (auto_linked, manual_linked, potential_duplicate, unlinked, rejected), version, superseded_by_link_id, decided_by, decided_at. You have up to 40 million source identities and 60 million decisions applied in decided_at order. Build a structure answering which enterprise identity a given (assigning_authority, source_person_id) resolves to after any prefix of the log, and supporting a revert of the k most recent merges at O(1) each. State the query complexity, and say why path compression is unavailable to you.
Approach
- Intern the node key as the pair (assigning_authority, source_person_id) into a dense integer index. Never key on source_person_id alone: two facilities in one network routinely issue the same medical record number to different people, so a bare-value key merges two patients before any matching logic has run, and a single-source test fixture will never show it.
- Classify the log rows before touching the structure. auto_linked and manual_linked are unions; potential_duplicate and rejected are recorded decisions that must not union anything; unlinked is a revert of the link it supersedes, not a new edge.
- Use union by size with an explicit undo stack, pushing (attached_root, its previous parent, the previous size of the absorbing root) on every union. Find walks parent pointers to the root: union by size bounds tree height at log2(n), so a query is O(log n) worst case, a union is O(log n), and a revert pops two words and is O(1).
- Path compression is the thing you have to give up, and the reason is specific: it rewrites parent pointers of nodes that were never named in the union being recorded, so the undo entry no longer describes the mutation that happened and a revert restores a forest that is quietly wrong. Near-constant find is only available to an append-only structure.
- Hold enterprise_person_id as a property of the root slot rather than a value copied onto every member. A merge then relabels one slot instead of n rows, and an unmerge restores two labels instead of reconstructing n.
- State the price plainly: every read path gains a resolution hop and no downstream table can carry a plain patient_id column. That cost is what buys reversibility, and it belongs in the design discussion rather than being discovered later by whoever writes the first join.
Follow-up
- A merge is found wrong three weeks and 200,000 unions later, and your stack only reverts a suffix. What do you do instead, and what does that cost?
- A claim was posted under the losing identity before the merge. After the unmerge, which identity owns it, and what in your design let you answer that?
- Two matcher instances submit unions concurrently. What is the smallest change that keeps both the structure and the undo stack consistent?
Bitemporal coverage table with retroactive termination queries
You maintain coverage_span: coverage_id, enterprise_person_id, payer_id, plan_id, effective_date, termination_date (nullable, meaning open-ended, not unknown), status, valid_from timestamptz, valid_to timestamptz DEFAULT 'infinity'. An enrolment file received on 2026-03-05 terminates a member effective 2026-03-01, after a claim for service date 2026-03-03 was already adjudicated against the answer then in force. State your termination_date convention, write the two SELECTs — (a) is the person covered on 2026-03-03 as known now, (b) what the system believed on 2026-03-04 — and say precisely what the loader writes when the file lands.
Approach
- Separate the two time axes explicitly before writing anything: effective_date/termination_date are business time (when benefit applied), valid_from/valid_to are system time (when this row was the system's belief). Every query names both, or it is ambiguous.
- Pin the inclusivity convention, because the predicate depends on it. Enrolment files conventionally carry termination_date as the last covered day, so the business-time predicate is effective_date <= :svc AND (termination_date IS NULL OR termination_date >= :svc). If your feed uses an exclusive end, it becomes > :svc, and a half-open daterange column removes the ambiguity permanently.
- Write the as-known-now query by pinning system time to the open version (valid_to = 'infinity') and letting business time float to the service date.
- Write the as-of-belief query by pinning system time to an instant: valid_from <= :asof AND valid_to > :asof. Half-open on both ends is what makes 'exactly one row per key per instant' provable.
- Describe the write as one guarded statement rather than two independent ones: a data-modifying CTE whose UPDATE stamps the open row's valid_to to now() (the only UPDATE the design permits — it stamps system time, it never edits business facts) and returns that row's keys, with the INSERT of the new version selecting FROM that CTE. Feeding the INSERT from the UPDATE's RETURNING is what makes a replayed file a no-op; an unconditional INSERT leaves the partial unique index to raise 23505 instead, which is a backstop rather than idempotency. The pre-correction row stays readable either way, which is what makes the already-adjudicated claim defensible.
Worked solution 25 min
- Write the DDL with both ranges half-open in system time: valid_from timestamptz NOT NULL, valid_to timestamptz NOT NULL DEFAULT 'infinity', and a partial unique index on (enterprise_person_id, plan_id, effective_date) WHERE valid_to = 'infinity' so only one belief is open per business period.
- Write query (a) against the open system-time version, with the business-time predicate on the service date.
- Write query (b) by replacing valid_to = 'infinity' with the half-open instant predicate, keeping the identical business-time predicate so the only thing that changed is which belief you read.
- Write the loader as one statement: WITH closed AS (UPDATE ... RETURNING ...) feeding INSERT ... SELECT FROM closed. Guard the UPDATE with valid_to = 'infinity' so a concurrent loader cannot close the same row twice, and with a termination_date IS DISTINCT FROM :new_term test so a replay of the same file matches no row and therefore inserts none. Under READ COMMITTED a second loader that blocks on the row lock re-evaluates the guard against the newly committed version, fails it, and writes nothing.
- Trace the 2026-03-05 file by hand: before it, one row (effective 2026-01-01, termination NULL, valid_to infinity); after it, two rows, and confirm (a) returns not-covered while (b) returns covered.
Follow-up
- The claim adjudicated on 2026-03-03 is now retroactively uncovered. What does reprocessing have to read — the belief at adjudication time or the current truth — and what does the remittance reversal reference?
- Add the constraint that stops two active coverage versions from overlapping in business time for the same person and plan, without blocking the superseded history rows.
- A later file reinstates coverage with the same effective_date. How many rows exist for that period afterwards, and which one does query (a) return?
Add a backfilled NOT NULL column to a live encounter table
encounter holds 900 million rows and is written continuously by the admit/discharge/transfer consumer. You must add admission_source_code text, backfill it per row from a lookup on the source message archive, constrain it to a fixed code set, make it NOT NULL, and add an index on (facility_id, admit_ts) — with no write outage and no statement blocked longer than one second. Give the ordered steps, the lock level each takes, why the obvious single ALTER is unavailable here, and the session setting that stops a DDL statement from stalling everything behind it.
Approach
- Say why the shortcut does not apply. Since PostgreSQL 11, ADD COLUMN with a constant default is metadata-only and cheap, but this value is derived per row from an archive lookup, so there is no constant to store and the table must be written row by row regardless.
- Add the column nullable first: catalog-only, ACCESS EXCLUSIVE for microseconds. The danger is not the statement's duration but the lock queue — a pending ACCESS EXCLUSIVE blocks every reader that arrives behind it, so a long-running report turns a microsecond DDL into a multi-minute outage. SET lock_timeout = '1s' before the ALTER and retry on failure; that converts a potential outage into a retried statement.
- Deploy dual-write before backfilling. The consumer must populate the column on every insert and transfer from that moment, or the backfill chases a moving tail forever.
- Backfill in bounded batches over primary-key ranges, a few thousand rows per transaction with a pause between, restricted to WHERE admission_source_code IS NULL so it is resumable and idempotent. One giant UPDATE holds a transaction open for hours, pins the xmin horizon so autovacuum cannot clean anything, and bloats the table by a full row version per updated row.
- Add the value constraint as CHECK (...) NOT VALID first — ACCESS EXCLUSIVE, no scan — then ALTER TABLE ... VALIDATE CONSTRAINT, which takes only SHARE UPDATE EXCLUSIVE and scans without blocking reads or writes.
- Reach NOT NULL without a blocking scan: add CHECK (admission_source_code IS NOT NULL) NOT VALID, VALIDATE it, then SET NOT NULL, which on PostgreSQL 12 and later uses the validated constraint as proof and skips the full scan; drop the now-redundant CHECK afterwards. Build the index with CREATE INDEX CONCURRENTLY, which takes SHARE UPDATE EXCLUSIVE, makes two passes plus a wait for concurrent transactions, cannot run inside a transaction block, and leaves an INVALID index behind on failure that must be dropped and rebuilt.
Follow-up
- CREATE INDEX CONCURRENTLY has been running for four hours and you need to cancel it. What state is the index left in, how do you detect it, and what do you run next?
- Your backfill is at 40% and the replica is 90 seconds behind. What do you change, and what do you measure to decide the new batch size?
- The code set gains a value two weeks later. Does your CHECK constraint or a lookup table with a foreign key make that change cheaper, and what does each cost on the write path?
How would you approach the design and logic of a heating system?
How would you approach the design and logic of a heating system?
Approach
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Can you explain the core principles and benefits of modular programmin…
Can you explain the core principles and benefits of modular programming?
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
What are the practical applications and risks of using void pointers?
What are the practical applications and risks of using void pointers?
Approach
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Paginate a versioned result feed a partner resyncs nightly
A partner analytics client pulls every new or corrected result for a patient panel each night from observation_result (observation_id, enterprise_person_id, accession_id, loinc_code, value_numeric, unit_ucum, result_status, collected_ts, issued_ts, version, supersedes_observation_id, created_at). Corrections insert a new version rather than updating a row. Panels produce 10^4 to 10^6 rows per run, and on a failed page the client retries that page alone. Specify the pagination contract - parameters, cursor contents, sort key, and the index that serves it - and state exactly what the client is guaranteed when a correction lands mid-walk.
Approach
- Reject OFFSET on two counts: page N scans and discards N rows, so a full walk of 10^6 is quadratic, and because corrections INSERT rather than UPDATE, the window shifts under the walk and rows fall between pages.
- Reject sorting on clinical time. collected_ts and issued_ts both let a correction for a specimen drawn last week sort behind a cursor that has already passed it, so the correction is never delivered. Feed order must be insertion order, not event order.
- Sort on (created_at, observation_id). The id tie-break is mandatory: if created_at defaults to now(), that is transaction start time in PostgreSQL, so an entire accession's fan-out inserted in one transaction carries identical timestamps and a timestamp-only cursor either repeats or skips them.
- Name the visibility hazard that keyset pagination alone does not fix: created_at order is not commit order, so a long-running transaction can make a row with a smaller created_at visible after the cursor has passed it. Either hold the cursor back behind a lag window sized above the longest write transaction, or assign the feed sequence at commit time and order on that.
- Serve it from a composite index on (created_at, observation_id), or (enterprise_person_id, created_at, observation_id) if the walk is panel-scoped. Each page is one index range scan costing O(log n + limit) and flat in page number.
- Return version rows, not a collapsed latest-per-analyte view. A sync client needs the correction event itself; DISTINCT ON (accession_id, loinc_code) ORDER BY version DESC is a read-time projection, not a feed. Ship supersedes_observation_id so the client can apply the supersession locally.
Worked solution 25 min
- Write the OFFSET query and the keyset query side by side and name the index each one needs.
- Insert two observations for one accession inside a single transaction, confirm they share created_at when the column defaults to now(), and show that a cursor holding only created_at either repeats or skips one of them.
- Construct the commit-order interleaving with two sessions: A inserts and holds its transaction open, B inserts later and commits, the reader's cursor advances past B, then A commits. Name the observation_id that is never delivered.
- Define the cursor payload and the response envelope, including the rule for rejecting a cursor whose filter set differs from the current request.
- Write two sentences of contract text: the delivery guarantee, and what the client must do when supersedes_observation_id points at a row it has already stored.
Follow-up
- The client crashed and resumes from a cursor issued six hours ago. What makes that safe, and when do you refuse a cursor outright?
- A result is retracted with result_status 'entered_in_error'. How does the partner learn to stop using a value it already stored?
- The partner wants a total count to size the run. What do you serve, and what does it cost at 10^6 rows?
Duplicate results appear only in the hour after interface restarts
About one in 40,000 ingested results produces a duplicate observation_result row. It happens only in the hour after an interface engine restarts and never reproduces in tests. The consumer checks a ledger table for the idempotency key, then writes the observation, then inserts the ledger row in a second transaction. The ledger has a unique index on the key, and the losing insert logs duplicate, ignoring. Adding a debug log between the check and the write made duplicates more frequent. Diagnose and fix.
Approach
- Run the query that splits the hypotheses before anything else: for one duplicate pair, compare their idempotency keys. Identical keys mean the key is right and mutual exclusion is broken. Different keys mean the key itself is wrong — a sender reusing or rotating control identifiers — which is a different bug with a different fix. Most time lost on this class of problem is lost by skipping this step.
- Measure the gap between the pair. created_at deltas in the tens of milliseconds, attributed to two different worker identifiers, mean two workers processed the same replayed message concurrently. Deltas of minutes or hours mean the ledger row was never durably written — rolled back, or written in a transaction that later aborted — and a later replay legitimately re-processed the message.
- Read the transaction boundaries rather than the isolation level. The check, the clinical write and the ledger insert span two transactions, so the window between the check and the ledger insert is unprotected at every isolation level, READ COMMITTED and SERIALIZABLE alike. The unique index protected the ledger; it never protected the observation, which had already committed by the time the conflict was detected.
- Explain why instrumentation moved it. The debug log widened the window between the check and the ledger insert, so more concurrent pairs landed inside it. Deleting the log narrows the window and hides the defect without fixing it — the same reason a single-threaded test suite is green, and the reason the incident correlates with restarts, when the broker redelivers a window of messages at once.
- Fix by turning check-then-act into one atomic conditional write: in a single transaction, INSERT INTO ingest_ledger (idem_key, ...) VALUES (...) ON CONFLICT (idem_key) DO NOTHING RETURNING idem_key, and if no row comes back, do nothing further. The observation insert belongs in that same transaction. Under READ COMMITTED the second inserter blocks on the unique index until the first commits and then sees the conflict, so the index — not the isolation level — supplies the mutual exclusion.
- Prove it with a deliberate concurrency test: two sessions released by a barrier on the same key, several thousand rounds, asserting exactly one observation row each time, then replay a captured restart window and diff row counts against a clean run.
Follow-up
- The clinical write must go to a different store than the ledger. How do you keep this atomic without a distributed transaction, and what does the outbox look like?
- The sender's control ID counter rolls over and begins reusing values. What breaks in your key, and what production signal would tell you it happened rather than a customer telling you?
Four days sample coding, design, fundamentals and the practical rounds at deliberately shallow depth, which is enough to surface the topics you did not know were in scope. That map, rather than a guess made on day one, decides where the last three days go.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Coding, one pass at shallow depth
- Solve one problem from each of six families, an array with two pointers, hash counting, binary search, a tree traversal, a graph traversal and one dynamic program, under a hard twenty-minute cap with no extensions, marking each finished, late, or stalled.
- For every stall, write the exact move you could not make rather than the subject, so the note reads could not turn the recurrence into a loop rather than bad at dynamic programming.
- Fix nothing today. The value of the pass is the unfixed record.
Deliverable: Six timed attempts marked finished, late or stalled, each stall carrying a named blocking move.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Design, one pass at shallow depth
- Spend twenty minutes each on three different shapes, a read-heavy feed, a write-heavy ingest path, and something needing a transaction across two entities, stopping each at requirements, interface and data model.
- After each, write the first question you could not answer, which is usually a number you could not estimate or a failure mode you had no vocabulary for.
- Mark which of the three you would be most relieved not to be asked, and treat that as data rather than as a preference.
Deliverable: Three shallow designs, each with the first unanswerable question written at the bottom.
Practice prompt ↗Practice prompt ↗03Fundamentals and the practical rounds
- Answer eight short questions in writing at four minutes each, covering the material that fills the gaps between the big rounds: what happens between a URL and a rendered page, what an index costs on write, when a process is preferable to a thread, and what conditions a deadlock requires.
- Do one thirty-minute practical task of the kind a take-home compresses: read an unfamiliar two-hundred-line file and write what it does, what you would change, and the one thing you remain unsure of.
- Score every answer fluent, correct but slow, or absent, and keep the absent ones visible.
Deliverable: Eight scored short answers and one written reading of unfamiliar code.
Practice prompt ↗Practice prompt ↗04The rounds that are about you, and the map
- Deliver three behavioural answers aloud against a timer, a conflict, a failure you owned, and a decision made without enough information, marking any that ran past three minutes or contained no number.
- Assemble the map: every marked item from days one to three on a single page, sorted by how likely it is to appear in your loop rather than by how uncomfortable it felt.
- Choose exactly two areas for the remaining three days and write down what you are deliberately abandoning.
Deliverable: A one-page scored map of the whole surface area with two areas chosen and the rest explicitly abandoned.
Practice prompt ↗Practice prompt ↗Worked solution ↗05First chosen area, to the depth you skipped
- Work the higher-ranked area in four focused blocks, choosing items one level above where you stalled rather than repeating what already works.
- After each block write the rule you extracted in one sentence with its precondition attached, since a rule carrying no precondition is exactly what fails under a variation.
- Re-attempt the day-one or day-two item that exposed this area and compare against the original timing.
Deliverable: Four worked blocks, a timed re-attempt against the original, and three one-sentence rules with preconditions.
Practice prompt ↗Practice prompt ↗06Second chosen area, where the gap is coverage rather than speed
- Treat the second area differently from the first. Day five drilled something you could already half-do; this one is usually a topic you had simply never met, so build one worked reference example end to end and keep it, rather than attempting six problems badly.
- Write down the vocabulary you were missing on day two or three, five terms at most, each with the one sentence that makes it usable in an answer rather than the textbook definition.
- Redo the shallow attempt that exposed this area and note whether you now fail later in the problem, because moving the failure point is the realistic gain from a single day and is worth more than a score that did not change.
Deliverable: One worked reference example for the newly covered area, a five-term vocabulary list, and a note on where the failure point moved.
Practice prompt ↗Practice prompt ↗07Reassemble the loop
- Sit two rounds back to back with no gap, ordering them so the area you chose second comes last, because the map was built from rested, isolated attempts and the loop will reach your weaker area when you are already spent.
- Write where the second round suffered from the first, which is normally the point at which structure collapses into narration.
- Reduce the week to one page holding only the rules you can state without reading them.
Deliverable: Mock notes on cross-round carryover plus a one-page card of rules you can recite from memory.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Every story you tell gets read for blast radius and judgement: what could have broken, who else it touched, what you knew at the moment you decided. Nobody can audit your code in an hour, so they audit your reasoning instead. Pick work where the call was genuinely yours and the consequences were real enough to remember.
What do you know about the mission and product line of?
What do you know about the mission and product line of?
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- Pick a story where you made the decision, not one where you watched it.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
What are your primary professional strengths and areas for development…
What are your primary professional strengths and areas for development?
Approach
- Name the disagreement and how you resolved it with evidence.
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
Reverse a fail-closed eligibility check at patient registration
You designed registration to block when the external eligibility inquiry exceeds its timeout budget, reasoning that an unverified registration becomes a denial weeks later. Months in, the payer's p99 sits near the budget, front desks are queueing, and two teams have built on the guarantee that a completed registration implies verified coverage. You reversed the decision. Walk through it: the evidence that changed your mind, what you deliberately kept fail-closed, how you migrated the teams relying on the old behaviour, and what you would change about how you made the original call.
Approach
- The probe is whether a reversal is backed by measurement or by fatigue. Lead with both costs in their own units: the share of inquiries exceeding the timeout and the wait minutes that produced at the desk, against the write-off dollars attributable specifically to registrations that proceeded unverified during the pilot.
- State what your original reasoning got wrong, concretely. The usual answer is that you priced the denial and not the queue, and that you reasoned about a p50 round trip when a fail-closed default is governed by the dependency's p99 and by its outage minutes, neither of which you had measured before choosing.
- Show the reversal was partial, which is what a good one looks like. Emergency presentations never block, because care is not conditioned on a benefits answer; high-cost elective services keep a hard stop where the write-off is large and the delay is tolerable; everything else proceeds with coverage_status 'unverified' into a work queue that must clear before the claim is submitted.
- Treat the work queue as part of the design rather than as an afterthought: what enters it, what clears it, what happens when it is not cleared before the submission deadline, and who is paged when its depth grows. A fail-open path with no queue is not a degradation strategy, it is a silent one.
- Describe the migration for the teams that depended on the guarantee: a per-encounter_class flag, a period where the new decision is computed and logged while the old behaviour still stands, and the consumers told which field replaces the assumption before the switch, not after.
- Close on the process change rather than the outcome — what you would now require before shipping any fail-closed default: a measured p99 from the dependency, a named owner for the blocked case, and a reversal trigger agreed in advance so the next reversal is a threshold being crossed rather than an argument.
Follow-up
- Your unverified queue is 900 deep on a Monday and the submission deadline is Wednesday. What happens, and who decided that in advance?
- How do you prevent 'unverified' from quietly becoming the normal path once the desks learn it is faster?
- Six months later the payer's p99 halves. Do you reverse back, and what evidence would make you?
- 01
What do you know about the mission and product line of?
- 02
What are your primary professional strengths and areas for development?
- 03
You designed registration to block when the external eligibility inquiry exceeds its timeout budget, reasoning that an unverified registration becomes a denial weeks later. Months in, the payer's p99 sits near the budget, front desks are queueing, and two teams have built on the guarantee that a completed registration implies verified coverage. You reversed the decision. Walk through it: the evidence that changed your mind, what you deliberately kept fail-closed, how you migrated the teams relying on the old behaviour, and what you would change about how you made the original call.
Is this an official SciCan interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at SciCan. Rounds and questions reflect what candidates have reported, not a process SciCan has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long is the typical interview process?
The process can vary, but you should expect multiple rounds of interviews. Some candidates have reported processes lasting across several hours or multiple sessions, so prepare for a comprehensive evaluation.
PracHub interview research ↗What is the best way to prepare for the technical rounds?
Review the projects on your resume thoroughly. Be prepared to explain the "how" and "why" of your code, focusing on how you solved specific technical challenges.
PracHub interview research ↗Is it important to know the company history?
Yes. Being able to speak to SciCan’s position in the infection control market and why you want to work on medical technology will demonstrate genuine interest and preparation.
PracHub interview research ↗What differentiates top candidates?
Beyond technical skills, the ability to communicate clearly and professionally is key. SciCan looks for engineers who are reliable team players and who take ownership of their work.
PracHub interview research ↗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.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
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