As a Software Engineer at NewYork-Presbyterian Hospital, you are not just writing code; you are building the digital infrastructure that supports world-class patient care. Your work directly impacts the efficiency of clinical workflows, the security of sensitive health data, and the overall experience of both healthcare providers and the patients they serve. You will be operating at the intersection of complex healthcare regulations and high-stakes software performance. This role requires a unique blend of technical precision and empathetic problem-solving. You will contribute to large-scale systems, potentially involving electronic health records (EHR) integration, data analytics platforms, or internal tools that streamline hospital operations. If you are driven by the opportunity to solve high-impact technical challenges in a mission-critical environment, this position offers significant professional growth and the chance to contribute to the future of healthcare technology. ##### Tip The healthcare domain is highly specialized. While your general engineering skills are paramount, demonstrating an understanding of how software reliability directly correlates to patient outcomes will set you apart from other candidates.
Recruiter Screening
reportedInitial screening to align on expectations and discuss the role.
What to demonstrate
- Initial screening to align on expectations and discuss the role
- Depth in SQL
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Assessments
reportedA series of technical evaluations to assess your skills and knowledge.
What to demonstrate
- A series of technical evaluations to assess your skills and knowledge
- Depth in SQL
How to prepare
- Answer aloud and timed: What are the primary considerations when building software that must comply with HIPAA or other data privacy regulations?
- Answer aloud and timed: How do you approach testing in a mission-critical environment where downtime is not an option?
Interviews with Hiring Team
reportedInterviews with team members to evaluate technical depth and cultural fit.
What to demonstrate
- Interviews with team members to evaluate technical depth and cultural fit
- Depth in SQL
How to prepare
- Answer aloud and timed: Describe a time you had to debug a significant issue in a production system.
- Answer aloud and timed: How would you design a big data solution to track patient throughput across multiple hospital departments?
Panel Interviews
reportedGroup interviews where you articulate your thoughts to multiple stakeholders.
What to demonstrate
- Group interviews where you articulate your thoughts to multiple stakeholders
- Depth in SQL
How to prepare
- Answer aloud and timed: What architectural patterns do you prefer for microservices, and why?
- Answer aloud and timed: How do you handle load balancing and system redundancy for enterprise-level applications?
Final Technical Evaluations
reportedFinal round of technical assessments focusing on system design and SQL.
What to demonstrate
- Final round of technical assessments focusing on system design and SQL
- Depth in SQL
How to prepare
- Answer aloud and timed: Describe the trade-offs between monolithic and distributed architectures in a healthcare setting.
- Answer aloud and timed: Describe a time you had to explain a complex technical issue to a non-technical stakeholder or clinician.
PracHub editorial advice for the preparation topics above.
Prepare for Panel Interviews
Practice explaining your technical decisions to a group. You will likely face a mix of technical leads and managers at once.
Master the Basics
Do not overlook SQL and standard data structures. These appear frequently in assessments.
Focus on Reliability
Always mention security and uptime in your architectural discussions. In a hospital, these are non-negotiable.
Be Proactive with Communication
If you have not heard back after a milestone, send a polite follow-up email to your recruiter.
Research the Mission
Familiarize yourself with the values of NewYork-Presbyterian Hospital to better align your answers with their organizational goals.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
How do you ensure data integrity when designing database schemas for high-traffic applications?
How do you ensure data integrity when designing database schemas for high-traffic applications?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
What are the primary considerations when building software that must comply with HIPAA or other data privacy r
What are the primary considerations when building software that must comply with HIPAA or other data privacy regulations?
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.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you approach testing in a mission-critical environment where downtime is not an option?
How do you approach testing in a mission-critical environment where downtime is not an option?
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.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How would you design a big data solution to track patient throughput across multiple hospital departments?
How would you design a big data solution to track patient throughput across multiple hospital departments?
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.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What architectural patterns do you prefer for microservices, and why?
What architectural patterns do you prefer for microservices, and why?
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.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe the trade-offs between monolithic and distributed architectures in a healthcare setting.
Describe the trade-offs between monolithic and distributed architectures in a healthcare setting.
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.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Edge instances grow 400 MB per hour until the nightly restart
Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.
Approach
- Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
- Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
- Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
- The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.
Follow-up
- Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
- How would you have detected this before an OOM, given the nightly restart masked the trend?
Built from the rounds and topics NewYork-Presbyterian Hospital candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the NewYork-Presbyterian Hospital loop
- Write out the reported sequence: Recruiter Screening, Technical Assessments, Interviews with Hiring Team, Panel Interviews, Final Technical Evaluations.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 5 reported rounds, with the weakest marked.
02Work SQL
- Spend the session on SQL, which NewYork-Presbyterian Hospital candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
03Work Data/Analytics Solutions (Big Data Architecture)
- Spend the session on Data/Analytics Solutions (Big Data Architecture), which NewYork-Presbyterian Hospital candidates report being tested on.
- Write one worked example in Data/Analytics Solutions (Big Data Architecture) and time yourself on it.
Deliverable: One timed worked example in Data/Analytics Solutions (Big Data Architecture).
04Work Big Data Solutions
- Spend the session on Big Data Solutions, which NewYork-Presbyterian Hospital candidates report being tested on.
- Write one worked example in Big Data Solutions and time yourself on it.
Deliverable: One timed worked example in Big Data Solutions.
05Answer out loud: Technical and Domain Knowledge
- Answer aloud, timed: How do you ensure data integrity when designing database schemas for high-traffic applications?
- Answer aloud, timed: Can you explain your experience with SQL and how you optimize complex queries?
Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: How would you design a big data solution to track patient throughput across multiple hospital departments?
- Answer aloud, timed: What architectural patterns do you prefer for microservices, and why?
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Problem-Solving
- Answer aloud, timed: Describe a time you had to explain a complex technical issue to a non-technical stakeholder or clinician.
- Answer aloud, timed: How do you handle conflicting priorities when working on multiple projects?
Deliverable: Spoken answers to 2 reported Behavioral and Problem-Solving question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Can you explain your experience with SQL and how you optimize complex queries?
Can you explain your experience with SQL and how you optimize complex queries?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 time you had to debug a significant issue in a production system.
Describe a time you had to debug a significant issue in a production system.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
How do you handle load balancing and system redundancy for enterprise-level applications?
How do you handle load balancing and system redundancy for enterprise-level applications?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 time you had to explain a complex technical issue to a non-technical stakeholder or clinician.
Describe a time you had to explain a complex technical issue to a non-technical stakeholder or clinician.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
How do you handle conflicting priorities when working on multiple projects?
How do you handle conflicting priorities when working on multiple projects?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Tell us about a challenging team dynamic you navigated and how you resolved it.
Tell us about a challenging team dynamic you navigated and how you resolved it.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
How do you stay updated with emerging technologies while maintaining legacy systems?
How do you stay updated with emerging technologies while maintaining legacy systems?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
- 01
Can you explain your experience with SQL and how you optimize complex queries?
- 02
Describe a time you had to debug a significant issue in a production system.
- 03
How do you handle load balancing and system redundancy for enterprise-level applications?
- 04
Describe a time you had to explain a complex technical issue to a non-technical stakeholder or clinician.
How long does the entire interview process take?
While it varies, candidates have reported that the process can span several weeks, including multiple rounds of interviews and potential technical assessments.
NewYork-Presbyterian Hospital Software Engineer candidate reports ↗What is the best way to stand out during the interview?
Demonstrate a clear understanding of the impact your code has on the end-user. Candidates who tie their technical solutions to the goal of improving patient care or hospital efficiency consistently perform better.
NewYork-Presbyterian Hospital Software Engineer candidate reports ↗Are the technical interviews whiteboard-based or practical?
Expect a mix. You may be asked to solve problems on a whiteboard or via a shared screen, but be prepared for practical, scenario-based questions that test your real-world application of technical concepts.
NewYork-Presbyterian Hospital Software Engineer candidate reports ↗What is the team culture like?
The culture is described as professional and mission-driven. The team values organization, clear communication, and a commitment to reliability.
NewYork-Presbyterian Hospital Software Engineer candidate reports ↗What topics does NewYork-Presbyterian Hospital test in interviews?
NewYork-Presbyterian Hospital interviews most often cover SQL, Project Management, Case Study Analysis, Financial Analyst Role Knowledge, and Supply Chain Management (Procedural Area Supply Chain). The exact emphasis depends on the specific role you apply for.
NewYork-Presbyterian Hospital Software Engineer candidate reports ↗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.
- 01NewYork-Presbyterian Hospital Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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