The Software Engineer role at Flatiron Health is pivotal in shaping the future of oncology data analytics and technology. This position is not only about writing code; it involves creating innovative solutions that can have a profound impact on cancer treatment and patient care. As a Software Engineer, you will contribute to products that enable healthcare providers to make data-driven decisions, ultimately improving patient outcomes.
In this role, you will work closely with cross-functional teams, including product managers, data scientists, and healthcare professionals, to design and implement software solutions that address complex challenges in the healthcare space. Your work will directly influence how data is utilized, making it a critical position within the organization. The complexity of healthcare data, combined with the need for scalable and reliable software systems, makes this role both challenging and rewarding.
Expect to engage with cutting-edge technologies and methodologies while working in a culture that values collaboration, innovation, and continuous improvement. You will contribute to projects that not only advance technology but also support a mission-driven organization dedicated to improving the lives of patients and their families.
Phone Screening
reportedThe title covers product work, platform work, infrastructure, mobile and frontend, and those are different jobs with different loops behind them. A screening call is the cheapest place to find out which one the seat is, and asking reads as experienced rather than fussy. The questions that separate them: what the team is on call for, what the last three projects were, and whether any round happens inside an existing repository instead of a blank file. Then say which of that you have done and which you have not. Claiming the whole posting is the fastest way to be found out one round later.
What to demonstrate
- Whether you can locate your experience inside one flavour of the role honestly instead of claiming the entire requirements list
- Whether you name what you have not done, which an experienced screener reads as a level signal and can plan the loop around
- Whether what you want next matches what the seat is: someone who wants greenfield work landing on a team that mostly operates an existing system is a hire that leaves within the year
How to prepare
- Mark every line of the posting as done, adjacent or new, and write one sentence for each adjacent line naming the closest thing you actually built
- Split your last two years into rough percentages across feature work, operating and debugging live systems, and design or review, so a question about scope gets numbers rather than adjectives
- Bring three questions that discriminate between seats: what the team is paged for, how much of the work is changing existing code versus standing up something new, and what shipped in the last quarter
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
Onsite Interview
reportedWhere the day includes a partner from product, design or data, that conversation is weighted like the technical ones and prepared for least. They are deciding one thing: whether having you in the room makes their decisions cheaper. That means options with costs attached, not implementation detail and not "it depends". An estimate someone can plan against — a range, the assumption that would push it to the high end, and what you would drop to hit the low one — is worth more than a confident single number, which everyone present already knows is wrong.
What to demonstrate
- Whether an estimate comes as a range with the assumption most likely to break it, and states what a specific scope cut would actually buy
- Whether a technical constraint is handed over as a choice with consequences on their side, rather than as a verdict they have no standing to argue with
- Whether you establish what decision is on the table before proposing anything
- Whether risk is raised while it can still change the plan, with the trigger that would confirm it, instead of reported afterwards as a slip
How to prepare
- Take a project that shipped late and write the two-sentence warning you could have given three weeks earlier, naming what you would have needed decided at that point
- Rehearse one estimate out loud until it arrives in three parts: the range, the single assumption that would blow it, and the smallest thing you would cut to protect the date
- Rewrite an objection you have actually made — the "we can't do that" version — as two options with their costs, so the choice ends up with the person who owns it
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Flatiron Health Data Analyst Interview Experience — Passed Coding, Cut on the Final Case Round
Let me share a DA interview. I signed an NDA so I won't go into detail. From what I've seen on the forum, it feels like nobody has ever gotten an offer from this one... so I didn't have very high hopes going in, and sure enough, that's how it went. First was the Karat screen: they tested SQL, R, and some statistics knowledge. A week later I got the invite for the first round. Round 1: they asked…
Read full experiencePracHub 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.
Storing monetary amounts as floating-point numbers.
Binary floating point cannot represent values like 0.10 exactly, so summing allowed and paid amounts across millions of claim lines accumulates representation and rounding error. Reconciliation against a remittance file is exact to the cent, so the drift surfaces as a balance that is off by a few cents with no identifiable cause, and engineers lose days looking for a logic bug in code that is correct apart from its numeric type. Integer minor units or an exact decimal type removes the class entirely.
Abandoning working code to chase the optimal solution
Get the straightforward version correct, state its complexity, and only then optimise, keeping the working version until the faster one passes the same cases. A correct quadratic solution with a stated path to linear beats a half-written optimal one that never ran.
Finishing a solution without stating its complexity
Give time and space in the same breath as the code, and define n explicitly when there are two sizes, since n nodes and m edges are not interchangeable. Space is the half that gets skipped: count the auxiliary structures you allocate and the recursion stack at its deepest, not only the answer you hand back.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to determine if a string is a palindrome.
Write a function to determine if a string is a palindrome.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Walk one small example through your approach before writing the whole thing.
- Choose the data structure from the access pattern, not from familiarity.
Follow-up
- How does this change if the input no longer fits in memory?
- Which test case would catch an off-by-one here?
Solve a problem involving depth-first search on a binary tree.
Solve a problem involving depth-first search on a binary tree.
Approach
- State the target complexity and say which constraint rules the naive version out.
- Choose the data structure from the access pattern, not from familiarity.
- Walk one small example through your approach before writing the whole thing.
Follow-up
- What is the worst case, and how likely is it on real data?
- Which test case would catch an off-by-one here?
Given an array of integers, return indices of the two numbers such tha…
Given an array of integers, return indices of the two numbers such that they add up to a specific target.
Approach
- Name the brute-force solution and its complexity before improving on it.
- Restate the input: its shape, its size, and what is guaranteed about it.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- What is the worst case, and how likely is it on real data?
- How does this change if the input no longer fits in memory?
How would you find the intersection of two linked lists?
How would you find the intersection of two linked lists?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
Follow-up
- How does this change if the input no longer fits in memory?
- Which test case would catch an off-by-one here?
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.
Worked solution 15 min
- Take the separator characters from bytes 3 through 7 of the buffer and store them.
- Slice to the first segment terminator and split on the field separator into tokens.
- Read tokens at indices 2, 3, 6 and 9 for MSH-3, MSH-4, MSH-7 and MSH-10.
- Unescape each extracted value with the escape character you read in step one, then convert MSH-7 to UTC.
- Return the tuple, and the hash of it if the ledger stores a fixed-width key.
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?
Bitemporal coverage table with retroactive termination queries
You maintain coverage_span: coverage_id, enterprise_person_id, payer_id, plan_id, effective_date, termination_date (nullable, meaning open-ended, not unknown), status, valid_from timestamptz, valid_to timestamptz DEFAULT 'infinity'. An enrolment file received on 2026-03-05 terminates a member effective 2026-03-01, after a claim for service date 2026-03-03 was already adjudicated against the answer then in force. State your termination_date convention, write the two SELECTs — (a) is the person covered on 2026-03-03 as known now, (b) what the system believed on 2026-03-04 — and say precisely what the loader writes when the file lands.
Approach
- Separate the two time axes explicitly before writing anything: effective_date/termination_date are business time (when benefit applied), valid_from/valid_to are system time (when this row was the system's belief). Every query names both, or it is ambiguous.
- Pin the inclusivity convention, because the predicate depends on it. Enrolment files conventionally carry termination_date as the last covered day, so the business-time predicate is effective_date <= :svc AND (termination_date IS NULL OR termination_date >= :svc). If your feed uses an exclusive end, it becomes > :svc, and a half-open daterange column removes the ambiguity permanently.
- Write the as-known-now query by pinning system time to the open version (valid_to = 'infinity') and letting business time float to the service date.
- Write the as-of-belief query by pinning system time to an instant: valid_from <= :asof AND valid_to > :asof. Half-open on both ends is what makes 'exactly one row per key per instant' provable.
- Describe the write as one guarded statement rather than two independent ones: a data-modifying CTE whose UPDATE stamps the open row's valid_to to now() (the only UPDATE the design permits — it stamps system time, it never edits business facts) and returns that row's keys, with the INSERT of the new version selecting FROM that CTE. Feeding the INSERT from the UPDATE's RETURNING is what makes a replayed file a no-op; an unconditional INSERT leaves the partial unique index to raise 23505 instead, which is a backstop rather than idempotency. The pre-correction row stays readable either way, which is what makes the already-adjudicated claim defensible.
Worked solution 25 min
- Write the DDL with both ranges half-open in system time: valid_from timestamptz NOT NULL, valid_to timestamptz NOT NULL DEFAULT 'infinity', and a partial unique index on (enterprise_person_id, plan_id, effective_date) WHERE valid_to = 'infinity' so only one belief is open per business period.
- Write query (a) against the open system-time version, with the business-time predicate on the service date.
- Write query (b) by replacing valid_to = 'infinity' with the half-open instant predicate, keeping the identical business-time predicate so the only thing that changed is which belief you read.
- Write the loader as one statement: WITH closed AS (UPDATE ... RETURNING ...) feeding INSERT ... SELECT FROM closed. Guard the UPDATE with valid_to = 'infinity' so a concurrent loader cannot close the same row twice, and with a termination_date IS DISTINCT FROM :new_term test so a replay of the same file matches no row and therefore inserts none. Under READ COMMITTED a second loader that blocks on the row lock re-evaluates the guard against the newly committed version, fails it, and writes nothing.
- Trace the 2026-03-05 file by hand: before it, one row (effective 2026-01-01, termination NULL, valid_to infinity); after it, two rows, and confirm (a) returns not-covered while (b) returns covered.
Follow-up
- The claim adjudicated on 2026-03-03 is now retroactively uncovered. What does reprocessing have to read — the belief at adjudication time or the current truth — and what does the remittance reversal reference?
- Add the constraint that stops two active coverage versions from overlapping in business time for the same person and plan, without blocking the superseded history rows.
- A later file reinstates coverage with the same effective_date. How many rows exist for that period afterwards, and which one does query (a) return?
Return the displayable version of each analyte with a window function
observation_result is append-only: observation_id, enterprise_person_id, encounter_id, accession_id, placer_order_id, filler_order_id, loinc_code, value_numeric, unit_ucum, result_status in ('registered','preliminary','final','amended','corrected','entered_in_error'), collected_ts, issued_ts, version, supersedes_observation_id. A chart view needs the currently displayable value for each (accession_id, loinc_code) for one person over the last 90 days, excluding analytes whose newest version is entered_in_error but keeping analytes whose older versions were. Write the query, give the index that serves it, and say honestly which part of the plan the index cannot remove.
Approach
- Recognise the shape: this is a top-1-per-group over a version chain, not a filter. Pick the newest version first, then apply the status predicate to the winner — the order is the whole exercise.
- Use DISTINCT ON (accession_id, loinc_code) with ORDER BY accession_id, loinc_code, version DESC, observation_id DESC on PostgreSQL; the ORDER BY must start with the DISTINCT ON expressions or it is a syntax error. Use ROW_NUMBER() OVER (PARTITION BY ... ORDER BY version DESC, observation_id DESC) with an outer WHERE rn = 1 if you need portability, since a window function cannot be referenced in the WHERE of its own query level.
- Push only person and time into the inner WHERE. Pushing result_status <> 'entered_in_error' inside promotes a retracted result's predecessor back onto the chart, which is the bug the prompt is testing for.
- Index (enterprise_person_id, collected_ts DESC) so the 90-day slice is an index range scan rather than a scan of the person's whole history.
- Be straight about the limit: because collected_ts carries a range predicate, no b-tree index can also deliver rows pre-ordered by (accession_id, loinc_code, version DESC), so the sort stays in the plan. That is acceptable because one person's 90-day slice is hundreds to low thousands of rows, and it is a quicksort in work_mem rather than an external merge — verify that rather than assume it.
Follow-up
- A correction arrives carrying the same filler_order_id and the same version number as the row it corrects. What breaks, and what do you add to the sort key?
- The same query for a cohort of 50,000 people instead of one person: does DISTINCT ON still hold up, and at what point do you materialise a latest-version table with a partial index?
- How would you show the clinician that the displayed value was amended, given the chart query returns only the winner?
How would you architect a system for real-time patient data processing…
How would you architect a system for real-time patient data processing?
Approach
- Choose a partition key and say what query it makes expensive.
- Name the read and write paths separately; they rarely have the same bottleneck.
- State the consistency you need, and where you are willing to be stale.
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?
What components would you include in a microservices architecture for …
What components would you include in a microservices architecture for a healthcare platform?
Approach
- Name the failure you are designing for, then the recovery path.
- Choose a partition key and say what query it makes expensive.
- Fix the scope first: who calls this, how often, and what they do when it fails.
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?
How do you ensure data integrity in a software application?
How do you ensure data integrity in a software application?
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?
Shape a bulk enrolment endpoint where rows fail independently
A payer partner submits weekly enrolment as one call carrying 10^4 to 2x10^6 member rows that become coverage_span (coverage_id, enterprise_person_id, payer_id, plan_id, subscriber_id, effective_date, termination_date, coverage_order, status, valid_from, valid_to, source_file_id). Some rows fail identity resolution, some carry a termination date before their effective date, and the rest apply cleanly. The partner's loader must be able to retry only what failed, and cannot fix the file until next week. Design the submission contract: synchronous or asynchronous, the response shape, how a row is addressed, and what retrying the failed subset does to rows that already applied.
Approach
- Size the call before shaping it. 2x10^6 rows will not survive a request/response cycle under any sane timeout, so the contract is asynchronous: upload, 202 with a job resource, poll or subscribe. All-or-nothing is equally wrong, because one malformed row would reject two million good ones and the partner cannot resubmit for a week.
- Give every row a caller-assigned stable identity - the partner's file id plus line number - so a result addresses back to the input without positional matching. Positional indexes break the instant the partner resubmits a filtered file, which is exactly what the retry is.
- Make the results a separate paginated resource, not an inline array: GET /enrolment-jobs/{id}/results?status=failed&cursor= returns one entry per failed row with a reason code and the offending field. An inline error array is unbounded, and two million failures is not a response body.
- Classify per row into applied, rejected and needs_review, and keep the last two addressable rather than dropping them. A row that failed identity resolution is not invalid data, it is an unresolved person, and it must be re-drivable after a manual link without the partner resubmitting anything.
- Make the row the unit of idempotency: UNIQUE (source_file_id, partner_line_number), so a resubmitted overlapping file is absorbed. State the one case that is not a duplicate - the same key with changed content must supersede, which in a bitemporal table means closing the prior version's valid_to and inserting a new row with valid_from set, leaving effective_date and termination_date as the partner asserted them rather than rewriting business time.
Worked solution 30 min
- Decide the transport and show the arithmetic that rules out a synchronous body at two million rows.
- Write the job resource schema: state, counts by outcome, started and finished timestamps, and links to the result pages.
- Write the per-row result schema with the caller's row identifier, the outcome, the reason code and the offending field.
- Write the row-level idempotency key and its constraint, then trace a retry of the failed subset against a job where 90 percent applied.
- Write what happens to a row whose content changed since it applied: the valid_to close-out and the new version, with business dates untouched.
Follow-up
- Midway the job hits a run of failures caused by our own dependency being down. Does the job fail, pause or keep going, and what does the partner see?
- The partner resubmits the entire file instead of the failed subset. What happens?
- How does the partner learn that a row applied and was later superseded by a newer file, without diffing two full exports?
Morning eligibility spike saturates the outbound payer pool
The eligibility service caches answers keyed by (person, plan, service date) with a 24-hour TTL. Every weekday at 07:00 local, for roughly 90 seconds, p99 goes from 45ms to the 6s outbound timeout, the connection pool to external payers saturates, and payers begin returning 429. Cache hit rate dips to 55%. Payer-reported latency for the requests they do serve is unchanged. Name the mechanism, the evidence that separates it from a slow dependency, and the fix — including what you would not do to the TTL.
Approach
- Separate many distinct keys missing from one key missed many times concurrently. Log (key, outcome) and, over a ten-second window inside the spike, compute misses divided by distinct keys missed. A ratio near one is a cold cache and a capacity problem; a ratio of twenty or fifty is a stampede, where concurrent requests for the same key each perform their own origin fetch.
- Confirm the expiry is synchronised. Histogram the remaining TTL across live keys: a single absolute 24-hour TTL set during yesterday's 07:00 peak produces a spike in that histogram exactly 24 hours later, which is why the incident is punctual rather than proportional to load.
- Locate the saturation. Payer-side latency flat while client-observed latency climbs to the timeout means the queueing is on your side. Measure in-flight outbound requests against the pool maximum and the time spent waiting to acquire a connection. That is the evidence that rules out a slow dependency, and the 429s are a consequence of your fan-out rather than its cause.
- Remove the duplication first with single-flight coalescing keyed on (enterprise_person_id, plan_id, service_date): concurrent misses for one key make one origin call and the rest wait on its result. This alone collapses the misses-to-keys ratio and is independent of every other change.
- Then fix the synchronisation and bound the exposure. Use a TTL of minutes with plus or minus 20% jitter so expiries spread, plus a bounded stale-while-revalidate. State the worst case explicitly — a 300s TTL with a 10s stale window is at most 310s of staleness — and return the computed-as-of timestamp to the caller, because coverage terminates retroactively and a long TTL is a correctness decision, not a performance knob.
- Bulkhead the outbound path per payer so one slow or rate-limiting payer cannot consume the concurrency budget of requests destined for the others, and define what the service returns when it cannot reach the origin at all.
Follow-up
- What does the service return when the payer is unreachable and the cache is empty? Does registration proceed with an unverified flag or block, and who owns that decision?
- How would you warm the cache before 07:00 without the warm-up itself becoming the stampede — and which keys are even warmable given the service date is part of the key?
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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Conflict answers where you were right and everyone came round are the weakest ones. Stronger: the evidence you went and collected, what would have changed your mind, and what you did in the weeks after the call went against you. Implementing a design you argued against, properly, is a specific and checkable behaviour.
Describe a situation where you had to collaborate with a difficult tea…
Describe a situation where you had to collaborate with a difficult team member.
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 would you do differently if you ran that again?
What motivates you to work in healthcare technology?
What motivates you to work in healthcare technology?
Approach
- Close with what you would do differently, concretely.
- 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.
Follow-up
- What did you decide not to do, and why?
- How did you know your change caused the improvement?
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?
- 01
Describe a situation where you had to collaborate with a difficult team member.
- 02
What motivates you to work in healthcare technology?
- 03
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.
Is this an official Flatiron Health interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Flatiron Health. Rounds and questions reflect what candidates have reported, not a process Flatiron Health 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?
The interview process is rigorous but fair, with a focus on both technical skills and cultural fit. Candidates typically report needing several weeks of preparation.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate a solid understanding of software engineering principles, effective problem-solving skills, and a genuine interest in the mission of Flatiron Health.
PracHub interview research ↗What is the culture like at Flatiron Health?
The culture is collaborative and mission-driven, with a strong emphasis on teamwork and innovation. Employees are passionate about using technology to improve patient outcomes.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The process can take several weeks, depending on scheduling and the specific role. Candidates should be prepared for multiple rounds of interviews.
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 2026-09-24 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-24 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-24
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