The Software Engineer role at Medall Healthcare is a foundational position focused on building and maintaining the robust digital infrastructure that supports modern healthcare delivery. As a member of the engineering team, you are responsible for developing scalable, reliable software solutions that bridge the gap between complex clinical data and actionable insights for practitioners and patients alike.
Your work will directly influence the efficiency of healthcare operations. Whether you are working on data processing pipelines, health record management, or patient-facing applications, your contribution ensures that Medall Healthcare can provide accurate and timely services. This role is ideal for engineers who are passionate about applying technical rigor to the healthcare domain and who thrive in environments where precision, security, and user accessibility are paramount.
Initial Screening
reportedThe person on this call usually cannot evaluate your code and does not need to. They write a short paragraph, and that paragraph is what a hiring manager skims when deciding who to put on your loop. So the test is not whether your work was hard, it is whether a non-engineer can repeat it correctly. Name systems by what they did rather than by their internal codename, give each project a shape (what was breaking, what you changed, what happened after), and keep the whole walkthrough near ninety seconds. Depth that cannot survive a paraphrase reads as vagueness.
What to demonstrate
- Whether a non-engineer can restate your projects without distorting them, since their paraphrase is what travels to the hiring manager, not your sentences
- Whether each project has a shape rather than a stack list: the failure or constraint, the change you made, the result and how it was measured
- Whether you can say what was yours inside a team project without either inflating it or disappearing into the plural
How to prepare
- Rewrite each headline project as two sentences with no internal system names and no acronyms outside your company, then say them to someone outside engineering and have them repeat them back. Fix whatever came back wrong
- Attach one measured number to each project: the baseline, the change, and the window it was measured over. Where nothing was ever measured, say that plainly rather than reaching for a plausible percentage
- Time the background walkthrough against a clock. If it runs past two minutes, compress the earliest role to a single clause and spend the recovered time on the most recent one
Technical Evaluation
reportedMost of the time lost in this format is not lost to thinking. It goes to a standard-library call you half-remember, an off-by-one in a loop bound, and a debugging loop that mutates code at random until something passes. When output is wrong, stop re-reading the whole function: take the smallest input that reproduces it and walk the state through by hand, printing intermediates if the environment allows. Guessing at a fix without a failing case you understand is how a five-minute bug becomes twenty, and the clock does not pause while you do it.
What to demonstrate
- Whether you reach the right structure without a detour, and can write it from memory rather than only recall that one exists
- Whether overflow is considered where the language has fixed-width integers, since a signed 32-bit value stops at 2,147,483,647 and then wraps in Java, is undefined behaviour in C++, and does not arise in Python, whose integers grow instead
- Whether recursion depth is treated as a constraint on large inputs, given that CPython's default limit is 1000 frames and a deep recursion can exhaust the stack in any language where an iterative version would not
- Whether a failing case is isolated and explained before any edit is made to the code
How to prepare
- From an empty file and with no references open, implement the pieces you lean on most: a heap push and pop, an iterative DFS with an explicit stack, and a binary search whose midpoint is written lo + (hi - lo) / 2, which avoids the overflow that (lo + hi) / 2 can hit in a fixed-width integer type
- Time yourself on the ten library calls you look up most, such as sorting with a custom comparator, splitting and joining strings, and finding the next key at or above a value in an ordered map, until the lookup is gone
- Take a solution you know is broken and, before touching it, write one sentence naming the input, the expected value and the actual value. Repeat until you do it without deciding to.
Collaborative Interviews
reportedYou cannot drill a format you do not know, so put the preparation into material that travels. Three pieces of your own work, each rehearsed until you can take a follow-up you did not anticipate, will carry a conversation or a code walkthrough equally well. Specificity is what separates that from filler. A number needs its definition before it means anything: a p99 is over some window and measured at some hop, and a server-side figure excludes the queueing and network time a client would see. The number you cannot qualify is the one to leave out.
What to demonstrate
- Whether your examples carry detail only someone who did the work would hold, such as what the binding constraint actually was, which alternative you rejected and why it was worse, and what you measured on each side of the change
- Whether a number survives one follow-up, meaning you can say what it was measured over and whether it moved because of your change or merely alongside it
- Whether a failure is described with the specific change that followed it, rather than a lesson stated in general terms
- Whether your part in a team effort is stated accurately, including what other people did
How to prepare
- Write a page on each of three projects covering the constraint, the option you rejected, the measurement before and after, and what went wrong. Cut any line you cannot take a follow-up on, since you are writing the parts you will be pressed on rather than a summary.
- Recover the real figures while you still have access: request volume, data size, latency with its percentile and window, team size, timeline. Note where each came from, whether a dashboard, a design document or memory, and mark the estimates so you can say which they are out loud.
- Take your weakest project story to someone who works in a different area and have them ask why four times in succession. The point where you run out of answer is the part to go and re-read before the round.
Final Assessment
reportedCoding rounds mostly set a floor. They decide whether you clear the bar, not where you land on the ladder. Level tends to come out of the design discussion and the ownership stories, so the question worth auditing beforehand is whether the scope you describe matches the scope of the job. Work that stops at your own service, or a story whose hard part was writing the code rather than getting several people to agree on an interface, reads a level below where you think you are interviewing, and that gap is usually resolved downwards.
What to demonstrate
- Whether the largest thing you describe owning ran end to end — the decision, the migration path, the rollout, and what you did when it went wrong — or stopped at the change you merged
- Whether design answers include what you would not build, what you would defer, and what you would measure before committing, rather than only what the boxes are
- Whether a disagreement in a story was settled with something checkable — a benchmark, a prototype, a written proposal — instead of by seniority or by waiting it out
- Whether you can say which calls you made alone and which you escalated, and why the line sat where it did
How to prepare
- Write your largest piece of owned work as a timeline of decisions — who decided what, when, and what you did when the plan broke — then delete every sentence whose subject is "we" and see how much survives
- Take one system you know well and drill the migration answer: how old and new paths run side by side under live traffic, how you compare their outputs, what the rollback is once writes are going to both, and which step you would not automate
- Map each line of the ladder in the job posting to a specific thing you have done, find the line you cannot support, and prepare the closest evidence you have plus an honest account of the gap
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.
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.
Answering a debugging question with a guess instead of a bisection
Give a procedure that halves the search space at each step: confirm the symptom reproduces, establish the last known-good version, input or timestamp, then bisect over commits, over the data, or over the layers of the request path. A plausible cause with no way to confirm it is the same move whether it happens to be right or wrong, which is why it scores nothing.
Reading the constraints as preamble rather than as part of the problem
The bounds are usually there to eliminate the obvious approach: n up to 10^5 makes an O(n^2) scan roughly 10^10 operations, far outside any per-test time budget, and an input larger than memory rules out loading it at all. When a bound is not given, ask for it, then say out loud which approach it kills.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Group transfer-chained encounters into episodes with out-of-order arrival
You have up to 5 million encounter rows for a reporting month, in arbitrary order: encounter_id, enterprise_person_id, facility_id, encounter_class, admit_ts, discharge_ts (nullable), status, prior_encounter_id (nullable, set on transfer), source_event_ts. prior_encounter_id may reference a row that arrives later or never arrives. Group rows into episodes, one per maximal transfer chain, returning episode root, person, earliest admit_ts, latest discharge_ts (null if any member is still open) and member count. Report cycles instead of following them. Target O(N) expected time.
Approach
- Recognise the shape: every row has at most one prior_encounter_id, so this is a forest of parent pointers, not a general graph. A hash map from encounter_id to row is the whole index you need, and the work is O(N) expected rather than anything sort-shaped.
- Resolve roots by iterative pointer-following with write-back memoisation: from each node walk prior_encounter_id until you reach a node with no parent, a parent absent from the input, or a node whose root is already known, then stamp the discovered root onto every node on that path. Each node is finalised once, so total work stays O(N) even when one chain is very long.
- Do not recurse. A malformed feed can produce a chain tens of thousands deep, and a recursive resolver dies on stack depth taking the whole batch with it. An explicit loop with write-back is both faster and bounded.
- Detect cycles with three-state marking: unvisited, on the current path, finalised. On reaching a node that is on the current path, emit a data-quality record naming every encounter_id in the cycle and exclude that component from the episode output. Choosing an arbitrary root instead would produce an episode that looks plausible and will be believed.
- Keep a dangling prior_encounter_id distinct from a genuine root. It means the chain starts outside the window or has not arrived, so flag the episode left-truncated and carry that flag into the output: an episode with an unknown start cannot be used for length of stay or readmission counting without biasing both downward.
- Compute aggregates during the same pass that assigns roots: minimum admit_ts, maximum discharge_ts with null propagation so any open member leaves the episode open, and exclude cancelled and entered_in_error members from the aggregates while keeping them retrievable.
Follow-up
- Two encounters name each other as prior_encounter_id. What does your detector emit, and who needs to see it?
- One episode spans two facilities whose clocks differ by four seconds. Do you order on source_event_ts or admit_ts, and what does the choice cost?
- A chain is cut in half by the month boundary. What does this month's report say, and how do you make next month's agree with 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?
Reconstruct what the chart displayed at five million past instants
observation_result holds 200 million versions: observation_id, enterprise_person_id, filler_order_id, loinc_code, value_numeric, result_status, collected_ts, issued_ts, version, supersedes_observation_id. An incident review hands you 5 million queries of (enterprise_person_id, filler_order_id, loinc_code, as_of_instant). For each, return the version that was visible at that instant, meaning the one with the greatest issued_ts at or before it. Neither side fits in memory. The per-query scan is correct. Explain precisely why it is too slow, then give a plan with its complexity.
Approach
- Name the clock before naming an algorithm. Visibility is issued_ts, the release time. collected_ts is when the specimen was drawn and can precede release by hours, so ordering on it reports a correction as visible long before anyone could have seen it. No amount of index work rescues the wrong column.
- Be exact about why the naive plan fails, because the interviewer is testing whether you can tell arithmetic cost from I/O cost. Per query the chain scan is O(V_k) and the arithmetic is trivial, but 5 million independent lookups into a 200-million-row structure that does not fit in RAM is 5 million random reads. The job is bounded by seeks per query, not by comparisons, and buying a faster comparison changes nothing.
- Convert random access into sequential access. Hash-partition both sides on the chain key (enterprise_person_id, filler_order_id, loinc_code) into P shards sized to fit memory, sort each shard's versions by (chain key, issued_ts) and its queries by (chain key, as_of_instant), and sweep the pair in lockstep. Total O((V + Q) log(V + Q)) with external sort, replacing Q seeks with two sequential passes.
- Inside a chain the sweep is linear, not logarithmic, because queries are visited in ascending as_of order and the version pointer only moves forward: O(V_k + Q_k) per chain. Binary search per query is the better shape only when Q is small relative to V and the versions are already indexed and resident.
- Return the empty answer as a distinct outcome. A query whose as_of precedes the first issued_ts means nothing was displayed, which is not the same as the earliest value, and is frequently the exact fact the review is chasing.
- Return a version later marked entered_in_error if it was live at the instant asked about. Reconstructing the past means reporting what was on the screen, including what was wrong, and quietly substituting today's truth defeats the purpose of the exercise.
Worked solution 45 min
- Choose P so a shard fits in memory: at P equal to 256, each shard holds about 781,000 versions and about 19,500 queries.
- Hash-partition versions and queries on the chain key, writing both to shard files.
- Per shard, sort versions by (chain key, issued_ts) and queries by (chain key, as_of_instant).
- Sweep the two sorted streams together, advancing the version pointer while issued_ts is at or before as_of and answering from the last version passed.
- Emit an explicit empty answer when the pointer has not advanced past any version for that chain, and concatenate the shard outputs.
Follow-up
- Read replicas lagged 40 seconds at the time. Does your answer describe what the clinician actually saw, and how would you bound the difference?
- Serve the same question online for a single chart at a p99 under 50ms. What changes?
- One partner changed its filler_order_id format mid-year, so the chain key is not stable. How does that appear in your output, and how do you detect it rather than returning empty answers?
Fix a paid-amount report that double counts claim versions
claim_line: claim_line_id, claim_id, claim_version smallint, line_number, frequency_code in ('original','replacement','void'), enterprise_person_id, coverage_id, billing_provider_npi, service_from_date, procedure_code, units, billed_amount_cents bigint, allowed_amount_cents, paid_amount_cents, adjudication_status, UNIQUE (claim_id, claim_version, line_number). A monthly report sums paid_amount_cents and joins coverage_span on enterprise_person_id with date overlap to attribute a plan. It overstates payment by roughly 9%, and reconciliation against remittance fails. Name both causes, then write the corrected query returning paid cents and plan per billing_provider_npi for March 2026 service dates.
Approach
- Cause one: the SUM spans every claim version. A replacement supersedes a prior claim and a void cancels one, so a replaced claim contributes twice and a voided claim contributes once when it should contribute nothing. Resolve the surviving version per claim_id before aggregating, never by deleting superseded rows.
- Cause two: the coverage join is one-to-many. coverage_span carries a system-time version per coverage period, so a person with three beliefs about one period multiplies every claim line by three. The join is for a label, not for filtering, so it must not be allowed to change cardinality.
- Collapse the version chain with DISTINCT ON (claim_id) ... ORDER BY claim_id, claim_version DESC, then join the lines back on (claim_id, claim_version), and exclude the surviving version when its frequency_code is 'void'.
- Make the coverage lookup cardinality-safe with a LEFT JOIN LATERAL ... LIMIT 1 pinned to valid_to = 'infinity' and ordered by valid_from DESC. LATERAL with LIMIT 1 cannot fan out by construction; a plain join can, and no amount of later DISTINCT repairs a SUM that already doubled.
- Keep the money in integer minor units the whole way. SUM(bigint) returns numeric in PostgreSQL, so there is no overflow and no binary floating-point error; cast to a display type only at the edge.
Follow-up
- A replacement arrives before its original. Your surviving-version logic picks max(claim_version) — what does the report show in the window before the original lands, and does the number self-correct?
- Add a claim_line_adjustment table with several reason codes per line. What does that do to this query, and where do you aggregate to keep it safe?
- Reconciliation is still off by 400 cents. Which of the two causes could still produce that, and what is your next query?
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.
Worked solution 45 min
- Write the runbook as numbered statements with the lock level annotated next to each, and mark which ones may be retried safely.
- Set lock_timeout = '1s' and statement_timeout deliberately high for the VALIDATE steps, since they are long but non-blocking.
- Write the batch loop with an explicit key range and a resume predicate, plus a query that reports remaining NULLs so progress is observable.
- Sequence NOT NULL as CHECK NOT VALID, VALIDATE, SET NOT NULL, DROP CONSTRAINT, and state which of those four takes ACCESS EXCLUSIVE.
- Run CREATE INDEX CONCURRENTLY outside any transaction block and monitor pg_stat_progress_create_index through its phases.
- Write the rollback for each step, including the DROP INDEX CONCURRENTLY for a failed build.
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?
Explain how you handle error logging and system monitoring in a distri…
Explain how you handle error logging and system monitoring in a distributed architecture.
Approach
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
How would you design a system to handle high volumes of concurrent pat…
How would you design a system to handle high volumes of concurrent patient records?
Approach
- State your assumptions explicitly before working the problem.
- Work from the requirement backwards to the design.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Describe a technical project where you had to pivot your approach mid-…
Describe a technical project where you had to pivot your approach mid-way through development.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Model an order placement so a lost response stays recoverable
An ordering system POSTs a medication or lab order to /orders. It assigns a placer identifier; the performing system later assigns a filler identifier, and neither side controls both namespaces. The caller times out after 3s and cannot distinguish an order that never arrived, one that was accepted with the acknowledgement lost, and one still in flight. Duplicating an order is a patient-safety event and silently abandoning one is worse. Design the contract so the caller can always determine the true outcome without guessing, and specify its behaviour at the timeout, on retry, and after repeated failure.
Approach
- Remove the ambiguity at its source by letting the caller name the resource before it exists, so the identity of the attempt never depends on our response arriving. The placer identifier is already caller-assigned and unique within the caller's namespace, so the server key is (placer_namespace, placer_order_id) under a unique constraint. A timeout stops being an unknown and becomes a question with a stable key.
- Prefer the shape that makes the retry trivially safe. PUT /orders/{placer_namespace}/{placer_order_id} is idempotent by HTTP semantics and needs no key header; POST /orders needs an Idempotency-Key to reach the same place. Under either, the write is one INSERT ... ON CONFLICT DO NOTHING, because a check-then-insert lets two retries through under READ COMMITTED. A repeat with an identical canonical body returns the existing resource; a repeat with a different body is 409 rather than a silent replace, since an order is not a document to overwrite.
- Give the caller a read that resolves a timeout without writing: GET on the same key returns accepted, routed, filled with its filler_order_id, or rejected with a reason, and 404 means we genuinely never saw it. Without that read, the caller's only instrument is another write, which is precisely the behaviour being designed out.
- Be honest about what acceptance means. Accept is durably persisted and queued for the performing system, not performed, so return 202 with the order resource and a status the caller polls or subscribes to. Returning 201 for an order that is not yet routed makes the caller believe a stronger fact than is true, and the filler identifier arrives later on the performing system's own timeline.
- Write the caller's behaviour explicitly, because the contract is only half the design: at timeout, GET the key; on 404, retry the write with the same key; on 5xx, exponential backoff with full jitter up to a bounded attempt count; after five failures, stop and raise a human-visible alert carrying the placer identifier. A queued-for-human state is a better outcome than either a duplicate order or a silent drop, and this domain cannot absorb the drop.
Worked solution 40 min
- Draw the three timelines a 3s timeout can hide - request lost, work done and response lost, work still in flight - and mark what the caller can distinguish in each, with and without a caller-assigned key.
- Write the resource path, the unique constraint, and the single atomic statement that performs the write.
- Write the state machine the GET exposes, state what 404 means, and name the one state that must never be inferred from a timeout.
- Write the caller's pseudocode for timeout, retry, backoff and give-up, with the attempt count and the alert payload.
- Run two concurrent retries of the same placer identifier against a real database and confirm one order exists and both callers observe the same state.
Follow-up
- The performing system returns a filler identifier for an order we have no record of. What do you do with it?
- The caller reissues an old placer identifier for a genuinely different order after a counter rollover. What breaks, and how would you detect it?
- Where does the record of the failed attempts live, and what must it carry for an incident review?
One facility stops updating while aggregate ingestion lag looks fine
Encounters from one sending facility stopped updating at 02:10. Aggregate HL7 ingestion throughput looks normal and the consumer group's total lag is up only 4%. One consumer instance is pinned at 100% CPU. The error log shows the same message control ID repeating about 200 times a second with a parse error on a timestamp field. The stream is partitioned by (sending facility, encounter) to preserve per-encounter ordering. Give the ordered diagnosis, the fix, and what you must not do to the message.
Approach
- Look at lag per partition, never the aggregate. With 64 partitions, one fully stuck partition raises total lag by about 1.6% while that facility's encounters stop entirely, so the aggregate metric is structurally blind to head-of-line blocking. The alert that would have caught this is per-partition lag or a committed offset that has not advanced.
- Confirm the loop by grouping errors by offset or control ID. The same identifier repeating at the retry rate, with the partition's committed offset frozen, is a poison message. A growing set of distinct identifiers instead means a systematic parse regression, which points at a dialect change on the partner's side and has a different fix.
- Classify before acting: parse-time or apply-time failure. A parse failure has written nothing and can be parked safely. An apply failure may have committed part of its work, so you must confirm the write path is idempotent against a unique constraint on the idempotency key before you replay or skip anything.
- Fix the mechanism rather than the one message: bounded retries with exponential backoff and a maximum attempt count, then park the raw bytes, partition key and offset to a dead-letter store and advance the offset. Alert on dead-letter depth, because lag will not catch the next one either.
- Handle the ordering consequence explicitly. Silently skipping a message in a per-encounter ordered stream permanently drops a clinical event — a transfer, a discharge, a result release. Mark the encounter key as blocked and reconcile it, rather than letting later messages apply over a gap nobody recorded.
- Then repair the parser. Partial-precision HL7v2 timestamps are legal: 2026, 202609, 20260922 and 20260922143000-0700 are all valid, so the parser must carry a precision rather than assume fourteen digits plus an offset, and must reject with a typed error the dead-letter path can classify.
Follow-up
- What maximum attempt count do you choose, and what does it imply about a transient failure like a downstream store being briefly unavailable, which looks identical to a poison message for the first few attempts?
- After the parser is fixed, how does an operator replay parked messages for that encounter without re-applying the messages that already succeeded behind it?
Roughly ninety minutes on weeknights with one longer weekend block. The plan cuts scope rather than compressing everything, on the assumption that one thing finished per night beats four half-started.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and take a cold baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list and keep it visible all week.
- Take one twenty-five-minute coding problem and one fifteen-minute design prompt cold, and write the single sentence naming what blocked each, because those two sentences decide where the remaining evenings go.
- Set the week's rule: one thing finished every night, including the night you only have forty minutes.
Deliverable: A one-page scope with a not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Worked solution ↗02One pattern, written three times from blank
- Choose the single pattern most likely to appear in your loop and write it three times from an empty file rather than editing the previous attempt.
- On the third pass, write the invariant as a comment before the loop body and the complexity before the first line of code.
- Stop at ninety minutes even if the third version is imperfect, and write the one thing you would fix given another hour.
Deliverable: Three independent implementations of the same pattern plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗03One design, only to the depth you can defend
- Take one system shape and go only as far as requirements, interface and data model, refusing to draw a box you could not survive a follow-up about.
- Attach one number to each non-functional requirement, deriving it rather than asserting it, and write the assumption the number rests on.
- Write the one tradeoff you are choosing against and the observation that would make you reverse it.
Deliverable: One design at interface-and-schema depth with derived numbers and one written reversible tradeoff.
Practice prompt ↗Practice prompt ↗04Only the fundamentals you will have to defend
- Write, in under two hundred words each, the answers to the two questions that follow almost any implementation: why this structure and not the obvious alternative, and what happens to this code at a hundred times the input.
- Write what an index actually costs: faster lookups on the indexed columns against a write that now maintains a second structure, plus the cases where the planner declines to use it anyway, low selectivity, or a predicate wrapping the column in a function.
- Delete any answer you cannot deliver aloud in under a minute, since an answer that needs reading is not an answer you have.
Deliverable: Three written answers, each under two hundred words and each timed aloud.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a ninety-second and a four-minute version of your main project and time both aloud rather than reading them.
- Prepare the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be ready to say exactly where it came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in the weekend block
- Run a sixty-minute mock covering a coding round and a design round in one sitting with no break, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the twenty-minute warm-up you will actually do on the morning: one problem you can already solve from a blank file, one design you can narrate, and nothing you have never seen.
- Re-read only your own notes from this week and open no new material.
- Write the logistics down: the editor or shared document you will be working in, whether execution and lookups are permitted, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the design structure, the project numbers, and the logistics.
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.
Describe a time you had to optimize a slow-performing query or service…
Describe a time you had to optimize a slow-performing query or service.
Approach
- Name the disagreement and how you resolved it with evidence.
- Give the blast radius: what could have broken, and what you measured.
- Close with what you would do differently, concretely.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
Unblock an engineer whose deductible accumulator overshoots
An engineer on your team has a benefit-application path that occasionally leaves a member's accumulated deductible above the plan limit — a handful of times a day, never in tests. Their code SELECTs the remaining deductible, computes patient responsibility in application code, then UPDATEs the accumulator to an absolute value. They have spent three days adding logging and are now asking whether the database is losing writes. Describe unblocking someone in this position: what you did first, what you let them find themselves, and what you left them able to diagnose next time.
Approach
- The probe is whether you teach a method or hand over a patch. Reproduce before explaining: two sessions, both BEGIN, both SELECT the accumulator row, both compute, both UPDATE to an absolute value, both COMMIT. The second overwrites the first and neither errors. Fifteen minutes of that ends three days of logging and, more importantly, ends the theory that the database is at fault.
- Be precise about where the anomaly is and is not, because the imprecise version teaches the wrong lesson. A single UPDATE that does its arithmetic in SQL is safe under READ COMMITTED: the blocked statement re-reads the row after the first commits. The loss comes from computing the new value in application code across the statement boundary, so the UPDATE writes an absolute amount derived from a stale read.
- Lay out three fixes with their costs rather than naming one. SELECT ... FOR UPDATE serialises writers per member and holds the lock for the transaction, so nothing in that transaction may make an external call. SERIALIZABLE with a retry loop on SQLSTATE 40001 requires the operation to be safely retryable. Per-member partitioning removes contention entirely and adds routing and rebalancing.
- Let them pick and defend one. The outcome you want is that they can name the anomaly and the isolation level unprompted next time, not that this particular ticket closes today.
- Leave an artefact behind: the two-session reproduction checked in, so the next person meets the interleaving rather than the symptom, and the reasoning is not stored only in your head.
Follow-up
- A claim is reversed and the accumulator must be credited back. What amount, given the plan design may have changed since?
- They choose SERIALIZABLE. How do they make the retry safe when the transaction also wrote a claim adjudication row?
- How would you have noticed this before a member saw it, and what would that check cost per adjudication?
Argue against rewriting foreign keys during a person merge
A proposal on your team simplifies merges: on merge, UPDATE enterprise_person_id across encounter, observation_result and claim_line to the surviving identity, then delete the link-resolution step so a patient-scoped read becomes one equality predicate. The author brings a p99 improvement on the patient summary, which fans out across eight to twelve resource types. You argued against it. Reconstruct the argument: the failure you predicted, the evidence you brought, the cost of your own alternative that you conceded, and who made the call.
Approach
- The probe is whether you can oppose a real performance win without retreating to data-hygiene arguments. Name the operation the proposal removes: unmerge. Once enterprise_person_id is rewritten in place, the row no longer records which source identity asserted the fact, and the only pair of columns that could — assigning_authority and source_person_id — lives in the link table the proposal deletes.
- Make it concrete with money rather than with charts. A claim_line posted under the losing identity carries coverage_id pointing at that identity's enrolment span. Rewriting the person while leaving coverage_id yields a row asserting the surviving person was covered under a span that was never theirs, which is a financial statement that adjudication and reconciliation will both act on.
- Do not argue that merges are rare, because that concedes the premise. Bring the rate of merges, the rejection rate on manually reviewed links, and the lag between a wrong merge and its discovery — false merges are a property of probabilistic matching, so the design has to assume them rather than hope against them.
- Concede the read cost honestly, since that is where the proposal's benefit is. Resolution through a versioned link table turns one equality predicate into a lookup feeding a predicate over a set of source identities, which changes index shape and raises the composite read's p99, and the composite is the number that matters, not the per-resource one.
- Offer the option that captures most of the win: a materialised current-link projection refreshed inside the merge transaction, so reads stay one predicate while the versioned link table remains authoritative and unmerge stays a supported operation rather than a recovery exercise.
- Finish on the decision process, not the technical point: what you wrote down, whether you blocked the change, who owned the call, and what you did once it was made — including if it went against you.
Follow-up
- Your projection and the link table disagree after a failed merge retry. Which one is authoritative, and how does a reader find out?
- An unmerge lands eight months later. What has to happen to the claims adjudicated under the merged identity in between?
- The author says reversibility can be handled by a nightly backup restore. Take that seriously and say exactly what it does not recover.
- 01
Describe a time you had to optimize a slow-performing query or service.
- 02
An engineer on your team has a benefit-application path that occasionally leaves a member's accumulated deductible above the plan limit — a handful of times a day, never in tests. Their code SELECTs the remaining deductible, computes patient responsibility in application code, then UPDATEs the accumulator to an absolute value. They have spent three days adding logging and are now asking whether the database is losing writes. Describe unblocking someone in this position: what you did first, what you let them find themselves, and what you left them able to diagnose next time.
- 03
A proposal on your team simplifies merges: on merge, UPDATE enterprise_person_id across encounter, observation_result and claim_line to the surviving identity, then delete the link-resolution step so a patient-scoped read becomes one equality predicate. The author brings a p99 improvement on the patient summary, which fans out across eight to twelve resource types. You argued against it. Reconstruct the argument: the failure you predicted, the evidence you brought, the cost of your own alternative that you conceded, and who made the call.
Is this an official Medall Healthcare interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Medall Healthcare. Rounds and questions reflect what candidates have reported, not a process Medall Healthcare 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 depending on the specific team and your availability, but most candidates complete the process within 3 to 5 weeks. The process aims to move efficiently while leaving the interviewers enough time to get to know you.
PracHub interview research ↗What is the most important thing to prepare?
Focus on being able to explain the "why" behind your past technical decisions. Interviewers are less interested in memorized answers and more interested in how you think through problems and handle technical trade-offs.
PracHub interview research ↗Is this role fully remote?
Working arrangements vary by team and location; discuss specific location and hybrid requirements with your recruiter during the initial screening.
PracHub interview research ↗What defines a successful candidate at Medall Healthcare?
Successful candidates are those who combine strong technical execution with a genuine curiosity about how their work impacts patient outcomes. Candidates report that interviewers value engineers who are proactive, collaborative, and committed to continuous improvement.
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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