As a Software Engineer at Healthfirst (New York), you play a critical role in designing, building, and scaling digital platforms that directly impact millions of members and healthcare providers across the state. This position is vital to maintaining and modernizing the core technological infrastructure that powers health plan operations, digital provider collaboration programs, and secure member applications. You will operate at the intersection of modern engineering practices and complex healthcare systems, transforming rigid legacy frameworks into agile, cloud-enabled architectures.
Your daily work directly influences how patients receive care, how providers manage claims and authorizations, and how internal teams deliver vital services. Whether you are developing enterprise APIs, optimizing cloud systems, or building user-facing applications using advanced frameworks, your solutions must adhere to the highest standards of security, performance, and regulatory compliance. The scope of products you touch requires both technical versatility and a deep commitment to user-centric engineering in a highly regulated industry.
This role offers a unique combination of technical complexity and meaningful business impact. You will collaborate closely with product managers, enterprise architects, and clinical operations teams to solve unique scaling and data integration challenges. Expect a dynamic environment where intellectual curiosity, rigorous problem-solving, and a passion for healthcare innovation are essential to your success.
Phone Screening
reportedBefore anything technical happens, someone has to decide which rung of the ladder your loop is calibrated to, and that decision sets the bar for every round after it. It comes from how you describe scope, not from your title, because titles do not convert cleanly between companies. The weak version of the answer is team size and years. The strong version names the largest change you shipped where nobody reviewed the design, what would have broken if you had been wrong, and what you were paged for. Get the level said out loud on this call, because the range and the loop both follow from it.
What to demonstrate
- Whether the scope in your own account maps onto a level the team actually has an opening at, so a mismatch ends the process cheaply rather than after four interviewers have spent a day
- Whether your title needs re-mapping: the same word describes very different amounts of independent decision-making at a twenty-person company and a ten-thousand-person one
- Whether your compensation expectation can be filled at that level in the structure the role pays in, which is why the number gets asked for before any engineer is scheduled
How to prepare
- Write down two changes from the last two years: the largest one you designed with nobody reviewing the design, and the largest one where someone more senior did. Lead with the first when scope comes up, and be ready to say which parts of the second were yours
- Ask which level the loop is calibrated to and what changes at the level above it, then plan your weeks from that answer rather than from the posting
- Settle a total-compensation range beforehand with the split named, base against bonus against equity and its vesting period, so a question about numbers gets a number instead of the word market
Technical Assessments
reportedWhat this round decides is narrow: whether you can produce code that runs and is correct on inputs nobody showed you. An elegant solution that does not compile scores below a plain one that does, so write a correct brute force first, say out loud that you know its cost, and improve it with the working version still on screen. What separates strong answers is who finds the broken case. Trace your own code against an empty input, a single element, and duplicate keys before you say you are finished, because being told is far more expensive than noticing.
What to demonstrate
- Whether degenerate inputs get checked without being asked for: an empty collection, one element, every element equal, and the extreme value the input type allows
- Whether the complexity you state matches the code you actually wrote, including a sort or a copy sitting inside a loop
- Whether the finished answer is verified against the worked examples before you call it done, rather than assumed correct because the code reads correctly
How to prepare
- Take five problems you have already solved and, without running anything, write down what each returns for empty input, a single element, and all-duplicates. Then run them and count how many you predicted wrong.
- Drill the brute force as its own skill: on ten problems, write only the obviously-correct slow version and time how long it takes to get it passing. If that is more than a few minutes, that is what to practise, not the optimal version.
- Add a fixed last step before you submit anything, reading only the loop bounds and the initial value of each accumulator, which is where most off-by-one errors live
Behavioral Evaluations
reportedYour first answer is not really what is scored. It buys the follow-up questions, and those decide the round. An interviewer with fifteen minutes takes one thread and pushes on it four or five times, so a story you can only tell at a single level of detail collapses under the third why. That is an argument for fewer stories known deeply rather than one prepared per prompt. Four or five pieces of work you can still explain down to the code you changed and the argument you had about it will cover nearly anything asked in this round.
What to demonstrate
- Whether a story holds as the questioning moves from what you did to why that instead of the alternative, and then to what you would change knowing what you know now
- Whether you can re-cut a project to answer the question actually asked rather than delivering a rehearsed block that answers an adjacent one
- Whether your level of detail is chosen rather than habitual: going down to the schema when the question is about the data model, staying out of it when the question is about the person who disagreed with you
How to prepare
- Pick four projects and write the chain out four levels deep for each: what you did, why that, why not the alternative, and what would have to be true for the alternative to have won. Where you cannot reach the fourth level, you have a placeholder rather than a story
- Have someone ask why three times in a row on a single thread with nothing else added, and mark the point where you start repeating a sentence you already said. That point is where the interviewer stops learning anything
- Build a one-page index instead of an answer bank: the common prompts in this round (disagreement, a failure that was yours, thin requirements, a deadline you missed, work you inherited) mapped to which of your four projects you would use for each, so the choosing is done now rather than while an interviewer waits
Panel Interviews
reportedNobody in the room with you decides this. Interviewers typically write their rounds up separately, often before seeing anyone else's, and the outcome is settled later from those write-ups. A split panel gets resolved by whichever note carries specific evidence, so what you want out of each room is one concrete thing that person could write down: a bug you caught yourself, a trade-off you named, a decision you owned. The rest is arithmetic. The project you describe in a behavioural conversation is often the same system you sketched an hour earlier, and the two accounts have to agree.
What to demonstrate
- Whether the scale, team size and timeline you attach to a project hold steady when that project resurfaces in a different round
- Whether each interviewer leaves with a specific thing to cite rather than a general impression of competence
- Whether a trade-off you defended in one round survives a challenge in another, instead of being quietly swapped for the answer the new interviewer seemed to want
- Whether a question you have already answered earlier in the day gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page sheet per project fixing the figures you will quote — request volume, data size, team size, elapsed time, what broke — and say them aloud from the sheet until they come out identical every time
- For each round on the schedule, decide in advance the one sentence you want in that person's notes, then check in a mock that you said it outright instead of leaving it to be inferred
- Have someone ask you the same project question twice, an hour apart, and diff the two answers for numbers that moved or a trade-off that reversed
PracHub editorial advice for the preparation topics above.
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.
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.
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.
Writing code before the input contract is pinned down
Before the first line, state the types, the size bounds, whether duplicates, negatives or an empty input are possible, whether the input is sorted, whether you may mutate it, and what the function returns when nothing matches. Every one of those answers changes the code, and discovering one at minute twenty costs a rewrite you no longer have time for.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Flag a requester reading too many distinct charts per window
You consume access-audit records (event_ts, requester_id, enterprise_person_id, purpose_of_use, break_glass) at tens of thousands per second, non-decreasing in event_ts. For each requester, emit an alert the first time any sliding 600-second window contains reads of more than D distinct enterprise_person_ids. Break-glass reads count toward the window and are also reported separately. Return (requester_id, window_start_ts, distinct_count). Target O(1) amortised per event and memory proportional to the events resident in the window, not to the day.
Approach
- Per requester, hold a deque of (event_ts, person_id) and a hash map from person_id to its occurrence count inside the window, plus a running distinct counter. Push on the right; while the front is older than event_ts minus 600 seconds, pop it, decrement its count and erase the key when the count reaches zero, decrementing the distinct counter. Each event is pushed once and popped once, so the amortised cost is O(1) and the memory is O(W) for window occupancy W.
- Test the threshold immediately after each push and nowhere else. Between two consecutive events the window can only lose members as its left edge advances, so the maximum distinct count over all window positions is attained at a position whose right edge is an event. Checking at pushes is therefore exhaustive rather than a sampling approximation.
- Latch the alert per requester and re-arm only when the distinct count falls back below D, otherwise one busy stretch emits thousands of near-identical rows and the real signal is buried by its own volume.
- Bound memory both per requester and globally. A requester whose window legitimately holds tens of thousands of reads must not hold the process hostage, so cap the deque and degrade above the cap to an approximate distinct counter such as HyperLogLog, stating the error you accept in exchange.
- Count break-glass toward the window but carry it in its own output field. Break-glass has to succeed during an emergency, which is exactly why it must be the most visible path in the audit, and excluding it from the count would make the abuse route the quiet one.
- State the precondition: this is correct only while input is non-decreasing in event_ts. Out-of-order arrival needs a bounded-lateness buffer and a watermark, and dropping late events without a counter is the failure that hides itself.
Follow-up
- One region's events arrive up to 90 seconds late. What buffer do you add, and what does that do to alert latency?
- One person uses two requester accounts. What has to change in the key, and what new false positive does that introduce?
- How do you restore window state after a process restart without replaying the whole day?
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?
Extract an idempotency key from a raw HL7v2 message
An HL7v2 message arrives as a byte buffer up to 256 KB, segments terminated by carriage return (0x0D), first segment MSH. MSH-1 is the field separator character itself and MSH-2 holds the encoding characters, so splitting the MSH segment on the separator puts MSH-n at index n-1 for n of 2 or more. Return the idempotency key built from MSH-4 sending facility, MSH-3 sending application, MSH-10 message control ID and MSH-7 event timestamp, with escape sequences decoded in each value. Single pass, O(n). Do not hardcode the separator characters.
Approach
- Read the delimiters out of the message instead of assuming them: the byte immediately after MSH is the field separator, and the next field's bytes give component, repetition, escape and subcomponent separators in that order. Everything downstream uses those values, because a partner is entitled to send different ones and the common defaults are a convention, not a guarantee.
- Verify the first three bytes are MSH before anything else and reject otherwise, since a socket read can begin mid-message. Then bound the segment at the first terminator and split that slice only, applying the n-1 offset that the MSH segment alone requires.
- Decode escapes in one left-to-right pass per extracted value: on the escape character, read to the next escape character and map the codes for field, component, subcomponent, repetition and escape back to their literal characters. Treat an unterminated escape as a malformed message rather than dropping the tail silently.
- Normalise MSH-7 to UTC before it enters the key. The timestamp may carry an offset or omit one, and two spellings of the same instant must hash identically or the ledger stops deduplicating the moment a partner changes its formatter.
- Compose the key as the full tuple, not the control ID alone. The control ID is unique only per sending application and some senders roll the counter over, so facility plus application plus control ID plus instant is the smallest key that survives a rollover.
- Cost is O(n) time and O(k) space for the extracted values. Hashing the whole body is also O(n) but cannot distinguish a genuine resend from a corrected retransmission that reuses the control ID, which is a different event.
Follow-up
- One partner sends MSH-7 with no timezone offset and another sends it with an offset. What do you store, and what do you compare on?
- A sender rolls its control ID counter over and restarts from zero. Which component of your key absorbs that, and which message pairs would still collide?
- Segments arrive separated by line feed instead of carriage return, or with a trailing empty field. Which should the parser accept and which should it negatively acknowledge?
Reproduce and fix a lost update on a deductible accumulator
An accumulator row holds deductible_applied_cents bigint and plan_deductible_cents bigint, keyed by (enterprise_person_id, plan_id, benefit_year). The adjudicator opens a transaction, SELECTs the row, computes the member's share in application code, then UPDATEs the row to the absolute new total it computed, all under PostgreSQL's default READ COMMITTED. Two claim lines for one member adjudicate concurrently: both charge the member deductible, but the accumulator advances by only one of the two amounts, so the member is billed deductible again on a later line after the plan deductible has already been met. Write the exact two-session interleaving that produces it. Then give three fixes, each naming the lock or isolation level, the SQLSTATE you must handle, and the throughput cost.
Approach
- State the anomaly precisely. READ COMMITTED takes a fresh snapshot per statement, so it prevents dirty reads but permits a lost update when a transaction reads a value, computes outside the database, and writes back an absolute result. The vulnerable window is the round trip through application code between two statements, not the transaction boundary — the same logic expressed as one relative UPDATE is safe at this very isolation level.
- Write the interleaving as an ordered script both sessions can be replayed from, with the commit points marked, and make both writes absolute (SET deductible_applied_cents = :computed_total). The point is not that two writes happen — it is that the second write stores a total computed from a value that had already been superseded by the time it landed.
- Fix one, SELECT ... FOR UPDATE on the read: the second session blocks on the row lock and, under READ COMMITTED, re-reads the newest committed version when it unblocks, so its computation starts from the winner's total. No serialization error to handle. Cost is serialised throughput per member and a lock held for the transaction's whole duration, so nothing inside may call an external payer.
- Fix two, REPEATABLE READ or SERIALIZABLE with a retry loop: in PostgreSQL, REPEATABLE READ aborts the second writer of a row with SQLSTATE 40001, 'could not serialize access due to concurrent update'; SERIALIZABLE additionally aborts on read/write dependencies detected by SSI, also under 40001. The retry re-runs the whole transaction, so the claim application must be idempotent or keyed by claim_line_id, or a retry double-applies.
- Fix three, single-writer partitioning: route by hash(enterprise_person_id) to one consumer per partition, so no two transactions ever touch one accumulator. No locks, no retries; the cost is head-of-line blocking behind a slow claim, rebalancing during deploys, and losing the ability to scale a hot member.
- Name the cheapest option and its real catch. A single UPDATE ... SET deductible_applied_cents = LEAST(plan_deductible_cents, deductible_applied_cents + :amt) ... RETURNING deductible_applied_cents does not lose the update: under READ COMMITTED an UPDATE that hits a concurrently updated row blocks, then re-evaluates its expressions against the newly committed version, so both increments land. The catch is that the member's share is the delta this statement actually applied, which is the returned value minus the row's pre-image — and RETURNING does not expose the pre-image before PostgreSQL 18's OLD/NEW aliases. On earlier versions you must read that pre-image under the same row lock, which puts you back in fix one with a shorter critical section. The one-statement form is a clean fix only when the caller does not need the delta.
Worked solution 35 min
- Open two psql sessions against a scratch database and seed one accumulator at deductible_applied_cents = 40000, plan_deductible_cents = 100000.
- Run the interleaving by hand with both writes absolute, and confirm the row ends at 70000 while the two lines between them charged the member 55000 of deductible — the serial answer is 95000, so 25000 of applied deductible has been lost.
- Run the control: repeat the identical interleaving with both UPDATEs written as SET deductible_applied_cents = deductible_applied_cents + :amt, and confirm the row ends at 95000. That form does not lose the update at READ COMMITTED, which is how you know the defect is the read-modify-write in application code rather than the isolation level by itself.
- Re-run the absolute version with FOR UPDATE on both SELECTs and confirm session two blocks, re-reads 65000, and writes 95000.
- Re-run both sessions at REPEATABLE READ and capture the exact error text and SQLSTATE from the losing session.
- Write the retry wrapper and show what makes the retry safe: a uniqueness key on (accumulator_key, claim_line_id) in an application ledger, so a re-run of an already-applied line is a no-op rather than a second application.
- Write the one-statement LEAST form with RETURNING and say where the member's share now comes from, given RETURNING carries no pre-image before PostgreSQL 18.
Follow-up
- A claim is reversed six months later under a plan design that has since changed. What amount does the reversal subtract, and where is that number stored?
- Your retry loop hits 40001 repeatedly for one member during a batch window. What is the backoff, and at what point do you stop retrying and pend the claim?
- Which of the three fixes survives a retroactive eligibility change that invalidates everything applied in the last month, and what does the rebuild look like?
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?
Describe Hashing and its implementation patterns.
Describe Hashing and its implementation patterns.
Approach
- State the consistency you need, and where you are willing to be stale.
- Name the failure you are designing for, then the recovery path.
- Name the read and write paths separately; they rarely have the same bottleneck.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
Explain how a given sample of code works, including enums, structs, an…
Explain how a given sample of code works, including enums, structs, and protocols.
Approach
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
- Name the read and write paths separately; they rarely have the same bottleneck.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
Explain the ExpressibleByStringLiteral protocol and its use cases.
Explain the ExpressibleByStringLiteral protocol and its use cases.
Approach
- Name the read and write paths separately; they rarely have the same bottleneck.
- Name the failure you are designing for, then the recovery path.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What would you drop to keep the system up under load?
- What breaks first when traffic grows ten times?
Explain how to further improve a personal project that used SwiftUI an…
Explain how to further improve a personal project that used SwiftUI and Combine.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What would you drop to keep the system up under load?
- What breaks first when traffic grows ten times?
Refactor this code to more effectively parse a JSON sample.
Refactor this code to more effectively parse a JSON sample.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- 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?
How to remove duplicates from an array?
How to remove duplicates from an array?
Approach
- Work from the requirement backwards to the design.
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Identity resolution at registration peak with reversible merges
Identity resolution sits in front of registration: one to three thousand match calls per second at morning peak against tens of millions of person records, a 200ms budget, and an availability requirement higher than the systems calling it. It writes person_identity_link (enterprise_person_id, assigning_authority, source_person_id, match_score, link_status, version, superseded_by_link_id, decided_by, decided_at). Design candidate generation, the deterministic-then-probabilistic pipeline and its outcome bands, and what registration does when the service is degraded — all without making a merge irreversible.
Approach
- Kill the naive framing with arithmetic: scoring an inbound record against tens of millions of candidates is on the order of 10^7 comparisons per call at up to 3,000 calls per second, which no scoring function survives. Block first — several cheap candidate-generation passes such as phonetic surname with birth year, normalised phone, and normalised address with birth date — unioned, cutting the candidate set to tens. Recall is now bounded by the blocking keys, which is why several passes beat one clever one.
- Run deterministic rules first and short-circuit. An exact hit on (assigning_authority, source_person_id) is a btree lookup producing a link with no score at all. Probabilistic scoring runs only when deterministic fails, which keeps the median call in single-digit milliseconds and leaves the 200ms budget for the tail.
- Define three outcomes, not two: auto-link above an upper threshold, reject below a lower one, and potential_duplicate in between routed to human review. One threshold forces a choice between merging two different people and fragmenting one person's record, and the middle band is exactly where both errors live.
- Implement merge as a new link version rather than a rewrite. The losing identity's source records get new person_identity_link rows pointing at the winner, with superseded_by_link_id set on the prior rows; unmerge restores the previous versions. Rewriting downstream foreign keys in encounter and claim_line destroys the original attribution and makes unmerge guesswork. You accept an extra resolution step on every patient-scoped query in exchange.
- Degrade deliberately and asymmetrically. Registration cannot block on a matcher outage, so fall back to a read replica of the deterministic index, and when that also fails, issue a provisional enterprise_person_id marked unresolved with the encounter queued for resolution on recovery. Degrade toward fragmentation, which is reversible, never toward merging, which is only reversible because step four held.
- Close the read-your-writes gap the replica introduces: a link created seconds ago may be absent from the replica, so the same person registering twice in two minutes can acquire two provisional identities. A small write-through cache keyed on (assigning_authority, source_person_id) handles the common case without making the whole path synchronous.
Worked solution 45 min
- Compute comparisons per call with and without blocking, and state the candidate-set size each blocking pass yields.
- Write the deterministic short-circuit and the probabilistic path, with the two thresholds and what happens in the middle band.
- Write the merge as inserts into person_identity_link, then write the unmerge that reverses it, and trace a claim posted under the losing identity through both.
- Define degraded behaviour at two levels — replica-only and fully unavailable — and state which error each one deliberately prefers.
- Show the read-your-writes case and the cache that resolves it.
Follow-up
- Two different people sharing a name and birth date were auto-linked last month and a lab result crossed charts. Walk through the unmerge and what each downstream consumer is told.
- You want to raise the auto-link threshold. What do you measure first, on what labelled data, and what does the change cost the review queue?
Nightly adjudication throughput collapses with deadlocks on accumulators
During the nightly claims batch, throughput collapses from 4,000 to about 300 claim lines a second for minutes at a time. pg_stat_database.deadlocks climbs, the PostgreSQL log carries deadlock detected with SQLSTATE 40P01, and the application retries the aborted transactions. Benefit application and the reversal handler both take SELECT ... FOR UPDATE on the member's deductible accumulator row and on the out-of-pocket accumulator row. Diagnose in order, then give a fix that removes the cycle by construction rather than by retrying faster.
Approach
- Read the deadlock report before theorising. PostgreSQL logs both process IDs, the statement each was running and the relation and tuple each was waiting on, which names the two lock-acquisition orders directly. There is no need to guess which code paths collide or to reproduce it first.
- Separate deadlock from ordinary contention. pg_stat_database.deadlocks counts cycles, while log_lock_waits reports waits longer than deadlock_timeout that never form a cycle. Collapsing throughput with few recorded deadlocks means a hot row — one family plan's shared accumulator — is serialising the batch, which has a different remedy from a cycle.
- Account for the detector's cost. A cycle is only detected after deadlock_timeout, one second by default, so every deadlock burns at least that on both sides plus the retried work. Raising the timeout lengthens each stall; lowering it raises the frequency of the detector's checks. Neither is a fix, and both are common first answers.
- Reject the two reflex answers explicitly. SERIALIZABLE does not prevent deadlocks in PostgreSQL — row-level lock cycles still occur and serialization failures with SQLSTATE 40001 are added on top — and a larger connection pool simply puts more transactions in contention for the same rows.
- Remove the cycle by construction: make each member's benefit application take exactly one lock. Either fold both accumulators into a single row keyed (enterprise_person_id, plan_id, plan_year) and update it in one statement, or take pg_advisory_xact_lock over a hash of that key as the first statement of both paths. One lock cannot form a cycle with itself. Deterministic ordering via ORDER BY ... FOR UPDATE also works but depends on the chosen plan preserving that order, so it is a weaker guarantee; hash collisions in the advisory-lock variant only serialise two unrelated members, which is safe but costs throughput.
- Then bound the transaction. No external call may sit between acquiring the lock and committing, or the row is held for a network round trip and the hot member serialises the batch regardless of which deadlock fix you chose.
Follow-up
- A reversal arrives for a claim adjudicated under a plan design that has since changed. What amount does it subtract, where is that amount recorded, and is that still atomic under your single-lock design?
- Remove the FOR UPDATE entirely and show the exact two-session interleaving that produces a lost update under READ COMMITTED, then say why a single UPDATE ... SET remaining = remaining - $1 does not have the same problem.
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 ↗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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Team size, service count and tickets closed say very little. Seniority shows in the decision you owned: what you chose not to build, which constraint you traded away, whose objection you had to resolve before anything could move. A large project where you executed someone else's plan is a small story.
What do you know about Healthfirst (New York) and why do you want to w…
What do you know about Healthfirst (New York) and why do you want to work here?
Approach
- Give the blast radius: what could have broken, and what you measured.
- State the situation in two sentences and spend the rest on the reasoning.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a challenging time and what you did to overcome it.
Describe a challenging time and what you did to overcome it.
Approach
- Pick a story where you made the decision, not one where you watched it.
- Name the disagreement and how you resolved it with evidence.
- 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 Healthfirst (New York) and why do you want to work here?
- 02
Describe a challenging time and what you did to overcome it.
- 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 Healthfirst (New York) interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Healthfirst (New York). Rounds and questions reflect what candidates have reported, not a process Healthfirst (New York) has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process for a Software Engineer at Healthfirst (New York)?
The interview process is rigorous and thorough, combining technical depth with behavioral evaluation. While some conversational rounds are straightforward, technical and coding evaluations expect you to understand your tools and concepts deeply rather than relying on high-level summaries.
PracHub interview research ↗How much preparation time should I plan for?
Candidates should allocate at least two to four weeks of dedicated preparation. Focus on brushing up your data structures, reviewing your primary language's core protocols and idioms, and preparing structured STAR-format stories for behavioral questions.
PracHub interview research ↗What distinguishes successful candidates from those who are rejected?
Successful candidates combine strong technical execution with clear, articulate communication. They do not just write working code; they explain their thought process, acknowledge trade-offs, and demonstrate genuine curiosity about the company's healthcare mission.
PracHub interview research ↗What is the company culture like for engineering teams?
Engineering teams operate in a collaborative, mission-driven environment focused on modernizing healthcare access for New York residents. Teams value transparency, professional accountability, and continuous technical improvement.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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