At SEPHA, the Software Engineer role—often manifesting as a Controls Engineer, Application Engineer, or Field Service Engineer—is the technical core of the company's custom packaging solutions. Beyond writing code, you connect high-level software logic with the physical machinery used by SEPHA's global pharmaceutical and medical device manufacturing clients. Your work directly impacts the reliability, safety, and precision of automated systems that millions depend on.
This role is inherently multidisciplinary. You will operate at the intersection of software development, hardware integration, and client-facing problem solving. Whether you are designing custom automation logic, configuring standard product applications, or troubleshooting complex systems in the field, your contribution ensures that SEPHA maintains its reputation for operational excellence. It is a position for engineers who thrive on complexity and take pride in seeing their code manifest as tangible, high-impact physical outcomes.
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.
Storing monetary amounts as floating-point numbers.
Binary floating point cannot represent values like 0.10 exactly, so summing allowed and paid amounts across millions of claim lines accumulates representation and rounding error. Reconciliation against a remittance file is exact to the cent, so the drift surfaces as a balance that is off by a few cents with no identifiable cause, and engineers lose days looking for a logic bug in code that is correct apart from its numeric type. Integer minor units or an exact decimal type removes the class entirely.
Assuming admission, discharge and transfer messages arrive in the order the events happened.
Interface engines route by message type across separate queues and retry independently, so a discharge can land before the admission it closes and an update can land before the registration it modifies. Ordering has to come from the sender's event timestamp plus a per-encounter sequence, and the consumer has to apply out-of-order and late-arriving events correctly rather than rejecting them, because rejection turns a recoverable ordering issue into permanent data loss that nobody notices until a report is short.
Tests that assert on the implementation rather than the behaviour
Assert on what a caller can observe, not on the number of internal calls or the shape of a private field. A test that breaks on every refactor but still passes when the answer is wrong costs more than it protects.
Starting work without saying what you are about to spend time on
State the plan before executing it: the approach, roughly how long it will take, and what you intend to leave hand-waved. That gives the interviewer a chance to redirect you in ten seconds rather than watching you spend fifteen minutes on the wrong sub-problem.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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.
Worked solution 40 min
- Intern three nodes: A is (AUTH1, MRN-77), B is (AUTH2, MRN-77), C is (AUTH1, MRN-90).
- Apply union(A, C) from a manual_linked row, pushing its undo entry.
- Apply union(A, B) from an auto_linked row with a high match score, pushing its undo entry.
- Query all three, then pop one undo entry and query all three again.
- Assert the parent and size arrays element-wise against a snapshot taken before the second union.
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?
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.
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?
Sum surviving claim versions in one pass over unordered lines
You are streamed up to 50 million claim_line records in arbitrary order: claim_id, claim_version, line_number, frequency_code (original, replacement, void), enterprise_person_id, allowed_amount_cents. Every version of a claim shares its claim_id, and a replacement arrives as a higher claim_version. Return total allowed_amount_cents per enterprise_person_id, counting each claim once at its highest version and contributing zero when that version is a void. Amounts are non-negative int64 minor units. Target O(N) time and O(C) space for C distinct claims, single pass, no sort.
Approach
- Say the two grains out loud before writing anything: the version lives at claim grain, the money lives at line grain. Every wrong answer here comes from applying a claim-level rule with a line-level filter.
- Keep one hash map from claim_id to a small record (best_version, sum_cents, person_id, is_void). Per line: if claim_version is greater than best_version, reset sum_cents to this line's amount and overwrite the person and void flag; if equal, add to it; if lower, discard the line. That reset is what makes the pass order-independent, so a replacement arriving before its original still wins.
- Resolve the void at the end, not at ingest. A void only nullifies the claim if it is the surviving version, and zeroing on sight would let an earlier replacement that arrives later resurrect the money.
- Fold the C claim records into a person-keyed map in a second phase, O(C). Do not accumulate into the person total during the stream: you cannot subtract a superseded version you have already forgotten.
- Cost is O(N) time, O(C) space. The sort-based alternative, group by (claim_id, claim_version) and keep the max, is O(N log N) and needs the set resident; prefer it only when C approaches N and the map will not fit.
- Keep cents in int64 throughout. Fifty million lines at a realistic per-line ceiling stay four orders of magnitude below 2^63, so the integer type costs nothing and removes the float drift class entirely.
Follow-up
- You shard the stream across eight workers. Sharding by hash of claim_id works; what exactly breaks if you shard by enterprise_person_id instead?
- A replacement arrives for a claim_id whose original never appears in the batch. What does your map produce, and is that the financially correct answer or a reconciliation break?
- The same claim_id is reused by two different submitting organisations. How does the key have to change, and when would you have noticed?
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.
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?
Denormalise consent into a decision table with a stated staleness bound
Access governance answers 'may this requester read this person's data for this purpose' on every identified read — tens of thousands per second at a 5ms p99 — from consent_directive: consent_id, enterprise_person_id, version, scope, purpose_of_use text[], grantee_org_id (NULL meaning all organisations), permit boolean, effective_ts, expires_ts, revoked_ts, status. Evaluating the normalised version history per read misses the budget. Design the denormalised decision store and its key, state the revocation staleness bound in seconds and the mechanism that actually enforces it, and say which normalised rows remain the system of record and why.
Approach
- Derive the shape from the read, not from the source table. The read is a point lookup on (person, grantee organisation, purpose), so fan the purpose_of_use array out into one row per purpose and make those three columns the primary key. A GIN index on the array would serve containment queries nobody issues.
- Handle the wildcard concretely. grantee_org_id NULL means 'all organisations', and NULL never equals anything, so a nullable column in the key breaks both the primary key and the equality lookup. Store a sentinel org id of 0 for the wildcard and resolve specific-before-wildcard in the lookup, or the wildcard row is silently unreachable.
- Make refusal a row, not an absence. An explicit permit = false must outrank a permit for the same key, so carry a precedence rank (specific grantee beats wildcard, refusal beats permit at equal specificity) and resolve with ORDER BY rank LIMIT 1. Default deny when nothing matches, so a rebuild failure fails closed.
- Separate the two staleness mechanisms and be precise about which one is the bound. The in-process decision cache has a TTL; a revocation also publishes an invalidation. The publish shortens the typical case to milliseconds, but it can be lost, so the guaranteed bound is the TTL alone — quote that number, for example 30 seconds, and justify it against the cache hit rate you need to hold 5ms p99.
- Evaluate expires_ts at read time against the request clock rather than baking the decision. A cached permit whose expires_ts has passed is wrong regardless of invalidation, because no event fires when a timestamp simply goes by.
- Keep consent_directive as the system of record: audit must reconstruct the directive in force at any past instant, the decision table holds only current state, and a corrupted decision table must be rebuildable by replaying the directives. Bound the write amplification by keeping grantees at organisation granularity and purposes a closed code set, so one directive expands to a known small number of rows rather than an unbounded cross product.
Worked solution 40 min
- Write the decision table DDL with the three-column primary key, the sentinel wildcard, the precedence rank and a monotonic decision_version.
- Write the lookup query: equality on the key with the wildcard row unioned in, ORDER BY rank, LIMIT 1, and an expires_ts predicate evaluated against the request timestamp.
- Write the projection that turns one consent_directive version into its decision rows, and compute the expansion factor for a directive covering four purposes and one organisation.
- State the TTL, then justify it: at N reads per second and M people, compute the cache hit rate the TTL yields and check it against the 5ms p99 budget.
- Write the failure narrative for a lost invalidation and confirm the TTL, not the bus, is what caps exposure.
- Write the rebuild procedure from consent_directive and the assertion that proves the rebuild is complete before it is swapped in.
Follow-up
- Break-glass must always succeed and must be unmistakably marked. Where does it sit relative to this lookup, and what stops it from becoming the quiet default path?
- The invalidation bus is down for thirty minutes. Walk through what a revoked person's data exposure looks like minute by minute, and what the backlog does when the bus returns.
- Where does the access audit record get written so that an audit-store outage degrades neither the log's completeness nor the read's availability?
How do you ensure that your code meets safety compliance standards for…
How do you ensure that your code meets safety compliance standards for pharmaceutical manufacturing?
Approach
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Can you explain your process for integrating third-party sensors or ac…
Can you explain your process for integrating third-party sensors or actuators into a custom software framework?
Approach
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
How do you approach debugging a control logic sequence that is intermi…
How do you approach debugging a control logic sequence that is intermittently failing in a production environment?
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?
Resolving surviving claim versions with late-arriving originals
claim_line carries (claim_id, claim_version, line_number, frequency_code in original, replacement or void; billed, allowed, paid and patient-responsibility amounts as bigint minor units; adjudication_status). Batch feeds do not guarantee the original arrives before the replacement that supersedes it, and a void can reference a claim you have never seen. Financial totals must derive from exactly one surviving version per claim. Design the resolution as either query-time or materialised, the handling of a late original, and how nightly remittance reconciliation surfaces an error before finance does.
Approach
- Resolve, never delete. Define the surviving version as a function over the chain — the highest claim_version for a claim_id, with a void making the chain contribute zero — and keep every superseded row. Deleting them makes the replay requirement, reprocessing any claim from the archive to the same result, impossible to satisfy.
- Choose materialised over query-time for the totals path and say why. DISTINCT ON (claim_id) ... ORDER BY claim_id, claim_version DESC is fine for one claim and unacceptable as the basis of every financial aggregate over millions of lines. Maintain a claim_current table with one row per claim_id naming the surviving version, written in the same transaction that loads a version, and build aggregates from that join.
- Treat the late original as ordinary traffic. A replacement for an unknown claim_id inserts and marks the chain incomplete; when the original lands, resolution recomputes and the affected aggregates are invalidated by claim_id. Rejecting the orphan loses a financial fact permanently, because nothing is going to resend it.
- Keep money in integer minor units from the wire to the report. Binary floating point cannot represent 0.10 exactly, so summing millions of lines accumulates representation error, and reconciliation is exact to the cent — the symptom is a balance off by a few cents with no attributable cause and days spent hunting a logic bug in code that is correct apart from its numeric type.
- Reconcile by construction, not by eyeball: nightly, sum paid amounts over surviving versions for the remittance's date range and compare to the remittance total; on mismatch, bisect by billing provider and then by claim so the alert names specific claim_ids. An alert reading 'off by 412.19' is not actionable; one naming four claims is.
Worked solution 30 min
- Write the surviving-version function as SQL, then write the claim_current maintenance statement that runs on each version load.
- Construct a fixture where a replacement precedes its original and a void precedes both, and assert the totals after each arrival.
- Write the nightly reconciliation query and the bisect that turns a difference into a list of claim identifiers.
- Audit the path end to end for any float or double type touching a monetary value.
Follow-up
- A void arrives for a claim already paid and already reconciled in a closed period. What changes in the totals, and what does the next remittance say?
- Two replacements for one claim arrive in the same batch carrying the same claim_version. How do you break the tie, and what becomes of the loser?
Gateway instances OOM every five days with flat traffic
Interoperability gateway instances are OOM-killed with exit 137 every five to six days. Resident memory grows about 400MB a day per instance and never falls; restarting resets it. Message throughput, connection count and partner mix are unchanged over that period. The gateway keeps an in-process set of recently seen idempotency keys — (sending facility, sending application, message control ID, event timestamp) — in front of the durable ledger. Give the ordered investigation and say what evidence would rule the idempotency cache out.
Approach
- Establish that this is a retained-object leak before profiling. Compare resident memory with live heap measured immediately after a forced collection, sampled daily. Resident memory rising while post-collection live heap stays flat points at allocator fragmentation, off-heap or native buffers, or thread stacks, and a heap dump will implicate the wrong object. Rising post-collection live heap is a genuine leak.
- Find what the growth is linear in. Plot daily growth against messages processed, connections accepted and wall-clock hours across instances carrying different traffic. Proportional to messages means something is retained per message; proportional to connections means per-connection state is not released on abnormal close, such as a half-open socket whose FIN never arrives.
- Diff two heap dumps taken six hours apart on the same instance, sorted by retained size, and identify the dominator. An unbounded set or map shows up as one root holding millions of small entries. This is a five-minute answer once you have the dumps, which is precisely why the first two steps exist: they tell you whether the dump can answer the question at all.
- Check the idempotency cache directly against arithmetic rather than suspicion. At single-digit millions of messages a day and a key of roughly 80 bytes plus per-entry overhead, an unevicted set grows by hundreds of megabytes a day, which matches the observed 400MB. A live gauge of its entry count that stays flat is the evidence that rules it out; a gauge tracking messages processed convicts it.
- Fix and restore the invariant: bound the cache by size or by a time window, and keep the durable unique constraint as the authority. An in-process set is a latency optimisation and cannot be the correctness mechanism, because it does not survive a restart and is not shared across instances — so a replay right after a deploy would bypass it entirely.
- Verify over 48 hours that post-collection live heap is flat, and alert on the cache's entry-count gauge rather than on resident memory, which only tells you after the growth has already happened.
Follow-up
- How long must the time window be? Tie the number to the sender's replay behaviour after an interface restart and to how far back the broker redelivers.
- If growth had tracked connections instead of messages, what would you look at first, and how would a half-open MLLP connection show up in the socket table?
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.
Engineers over-index on what they repaired. A stronger answer covers something you knowingly left broken: the alert you tuned down, the data inconsistency you documented instead of chasing, the cleanup you deferred past two quarters. Give the reasoning and the condition that would have reopened it, so it reads as a decision and not as neglect.
What is your experience with PLC programming or industrial automation …
What is your experience with PLC programming or industrial automation software?
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- What did you decide not to do, and why?
Own an incident where the matcher linked two different people
You shipped the change that moved the identity matcher's auto-link threshold from 0.94 to 0.88, to cut the manual review queue. Six hours later a clinician reports another person's results on a chart. person_identity_link has roughly 4,000 auto_linked rows since the deploy, and the longitudinal record service resolves patient reads through that table, so every bad link is already widening chart reads. Describe an incident of this shape that you owned: detection, what you stopped first, how you reversed links that must stay reversible, and the durable change afterwards.
Approach
- The probe is whether you measure blast radius in the units the domain cares about. Open with the number of auto_linked rows written since the deploy, how many of those join two source records that disagree on a demographic the matcher did not weigh, and how many patient reads resolved through them — not with the root cause, which hides whether you could see the problem at all.
- Be honest that the false-link rate is not a count you can query. There is no ground-truth column saying two source records are the same human, so the first defensible number is precision on an adjudicated sample with a stated sample size, and everything downstream of it is an estimate.
- Separate stopping from fixing, and name both stop actions. Reverting the threshold halts new bad links within a deploy cycle and does nothing to the ones already written; any cached patient summary or materialised projection keyed on enterprise_person_id keeps serving the merged chart until it is invalidated.
- Say what reversal costs on this schema: new person_identity_link rows at version+1 with link_status 'unlinked', and superseded_by_link_id set on the bad rows. No DELETE, because the question an incident review asks later is what the index believed at a specific minute, not what it believes now.
- Escalate clinical exposure rather than data exposure. Enumerate the encounters and orders placed during the window against the affected enterprise ids, because someone may have acted on another person's result; that list, not the row count, is what goes to safety review.
- Close on what made the threshold reviewable afterwards: a shadow run scoring the candidate threshold against live traffic without writing links, a precision target measured on a labelled set, and an alert on auto-link rate per hour so the next one is caught by a metric rather than by a clinician.
Follow-up
- Two of the bad links were adjudicated and confirmed wrong, but a claim was already submitted under the merged identity. What do you do with that claim, and who decides?
- Your shadow run shows the old threshold also produces false links, at a lower rate. Does that change whether this was an incident?
- How would you have detected this in fifteen minutes instead of six hours, and what would that alert cost you in false positives during a normal registration peak?
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 is your experience with PLC programming or industrial automation software?
- 02
You shipped the change that moved the identity matcher's auto-link threshold from 0.94 to 0.88, to cut the manual review queue. Six hours later a clinician reports another person's results on a chart. person_identity_link has roughly 4,000 auto_linked rows since the deploy, and the longitudinal record service resolves patient reads through that table, so every bad link is already widening chart reads. Describe an incident of this shape that you owned: detection, what you stopped first, how you reversed links that must stay reversible, and the durable change afterwards.
- 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 SEPHA interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at SEPHA. Rounds and questions reflect what candidates have reported, not a process SEPHA has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long does the interview process typically take?
The timeline varies, but most candidates move through the stages within a few weeks. The process is kept efficient while making sure you meet the relevant members of SEPHA's engineering team.
PracHub interview research ↗What is the most important thing to prepare?
Focus on your past projects. Be ready to explain the "why" behind your technical decisions, especially regarding safety, reliability, and system performance.
PracHub interview research ↗Is there a coding test?
SEPHA may not run a traditional "whiteboard" coding test, but expect to discuss technical designs or troubleshoot hypothetical system scenarios that test your logical thinking.
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
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