At Oak St. Health, a Software Engineer plays a critical role in rebuilding healthcare from the ground up. Unlike traditional tech companies, the engineering team here builds and maintains the proprietary clinical application suite—including their custom electronic health record (EHR) platform, Canopy—which care teams use daily to coordinate care for Medicare patients. The software you build directly impacts the quality of preventative care, clinical workflows, and patient outcomes, making this role both highly visible and deeply meaningful. You will work on a modern, distributed architecture designed to handle complex clinical data pipelines, real-time patient tracking, and seamless integration with third-party medical services. The engineering team focuses on solving high-scale data challenges, developing robust microservices, and crafting intuitive user interfaces that reduce administrative burden for doctors and nurses. This position is ideal for engineers who thrive on practical problem-solving and want their code to have a tangible, positive impact on human lives. The technical challenges are diverse, spanning backend service orchestration, frontend state management, and enterprise-grade data integration. You will collaborate closely with clinical product managers, data scientists, and peer developers to deliver highly reliable software in a fast-paced, mission-driven environment.
Phone Screen
reportedInitial conversation with the hiring manager focusing on professional background and alignment with the company's mission.
What to demonstrate
- Initial conversation with the hiring manager focusing on professional background and alignment with the company's mission
- Depth in .NET
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 Evaluation
reportedIncludes a practical take-home assignment followed by a series of technical interviews.
What to demonstrate
- Includes a practical take-home assignment followed by a series of technical interviews
- Depth in .NET
How to prepare
- Answer aloud and timed: What are the differences between various dependency injection lifetimes in.NET, and when would you use each?
- Answer aloud and timed: Describe your approach to designing and securing a production-ready REST API.
Technical Interviews
reportedFocus on code comprehension, data manipulation, and frontend architecture.
What to demonstrate
- Focus on code comprehension, data manipulation, and frontend architecture
- Depth in .NET
How to prepare
- Answer aloud and timed: How do you handle exceptions and log errors across a distributed backend architecture?
- Answer aloud and timed: How do you manage complex application state in a frontend framework like Vue.js or React?
Panel Interview
reportedFinal stage with peer engineers and leadership discussing system design and behavioral scenarios.
What to demonstrate
- Final stage with peer engineers and leadership discussing system design and behavioral scenarios
- Depth in .NET
How to prepare
- Answer aloud and timed: Walk me through the best practices for structuring reusable components in a modern JavaScript framework.
- Answer aloud and timed: Have you used integration platforms like Workato to connect disparate business systems? If not, how would you design a custom integration layer?
PracHub editorial advice for the preparation topics above.
Focus on readability over cleverness
When writing or reviewing code during your technical sessions, prioritize clean, readable, and maintainable code over overly complex, "clever" one-liners. Explain your variable naming, component structure, and error-handling choices.
Demonstrate empathy for the end-user
Remember that the software you build is used by doctors, nurses, and clinical staff to care for elderly patients. Show that you think about user experience, system reliability, and minimizing administrative friction.
Be prepared to discuss trade-offs
No system design is perfect. Whenever you propose an architectural pattern, a database schema, or a state management solution, proactively explain the pros and cons of your approach and why it fits this specific scenario.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Walk through a data manipulation exercise where you parse, clean, and restructure an unstructured JSON payload
Walk through a data manipulation exercise where you parse, clean, and restructure an unstructured JSON payload.
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.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
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?
Write a SQL query to retrieve and aggregate patient data from multiple relational tables.
Write a SQL query to retrieve and aggregate patient data from multiple relational tables.
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?
How do you approach database indexing and query optimization when dealing with slow-running reports?
How do you approach database indexing and query optimization when dealing with slow-running reports?
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?
Explain the execution behavior of a provided snippet of.NET code and identify any potential performance bottle
Explain the execution behavior of a provided snippet of.NET code and identify any potential performance bottlenecks or bugs.
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 safely and efficiently share data between independent microservices in a distributed system?
How do you safely and efficiently share data between independent microservices in a distributed system?
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 are the differences between various dependency injection lifetimes in.NET, and when would you use each?
What are the differences between various dependency injection lifetimes in.NET, and when would you use each?
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?
Describe your approach to designing and securing a production-ready REST API.
Describe your approach to designing and securing a production-ready REST API.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you handle exceptions and log errors across a distributed backend architecture?
How do you handle exceptions and log errors across a distributed backend architecture?
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?
How do you manage complex application state in a frontend framework like Vue.js or React?
How do you manage complex application state in a frontend framework like Vue.js or React?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Walk me through the best practices for structuring reusable components in a modern JavaScript framework.
Walk me through the best practices for structuring reusable components in a modern JavaScript framework.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Have you used integration platforms like Workato to connect disparate business systems? If not, how would you
Have you used integration platforms like Workato to connect disparate business systems? If not, how would you design a custom integration layer?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you optimize frontend performance when rendering large lists or processing heavy data payloads?
How do you optimize frontend performance when rendering large lists or processing heavy data payloads?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Describe how you would design a test suite for a data ingestion pipeline to ensure data integrity.
Describe how you would design a test suite for a data ingestion pipeline to ensure data integrity.
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?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics Oak St. Health candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Oak St. Health loop
- Write out the reported sequence: Phone Screen, Technical Evaluation, Technical Interviews, Panel Interview.
- 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 4 reported rounds, with the weakest marked.
02Work .NET
- Spend the session on .NET, which Oak St. Health candidates report being tested on.
- Write one worked example in .NET and time yourself on it.
Deliverable: One timed worked example in .NET.
03Work REST APIs
- Spend the session on REST APIs, which Oak St. Health candidates report being tested on.
- Write one worked example in REST APIs and time yourself on it.
Deliverable: One timed worked example in REST APIs.
04Work SQL
- Spend the session on SQL, which Oak St. Health candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
05Answer out loud: .NET & Backend Engineering
- Answer aloud, timed: Explain the execution behavior of a provided snippet of.NET code and identify any potential performance bottlenecks or bugs.
- Answer aloud, timed: How do you safely and efficiently share data between independent microservices in a distributed system?
Deliverable: Spoken answers to 2 reported .NET & Backend Engineering question(s), under time.
06Answer out loud: Frontend & Integration
- Answer aloud, timed: How do you manage complex application state in a frontend framework like Vue.js or React?
- Answer aloud, timed: Walk me through the best practices for structuring reusable components in a modern JavaScript framework.
Deliverable: Spoken answers to 2 reported Frontend & Integration question(s), under time.
07Answer out loud: Data & Problem Solving
- Answer aloud, timed: Write a SQL query to retrieve and aggregate patient data from multiple relational tables.
- Answer aloud, timed: Walk through a data manipulation exercise where you parse, clean, and restructure an unstructured JSON payload.
Deliverable: Spoken answers to 2 reported Data & 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.
Describe a time when you had a technical disagreement with a peer or supervisor. How did you handle it, and wh
Describe a time when you had a technical disagreement with a peer or supervisor. How did you handle it, and what was the outcome?
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?
Walk me through a complex technical project you led or contributed to significantly. What were the trade-offs
Walk me through a complex technical project you led or contributed to significantly. What were the trade-offs you had to make?
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?
Why do you want to work at Oak St. Health, and how do you see your role contributing to our patient-first miss
Why do you want to work at Oak St. Health, and how do you see your role contributing to our patient-first mission?
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 me about a time you had to quickly learn a new tool or technology to solve an urgent production issue.
Tell me about a time you had to quickly learn a new tool or technology to solve an urgent production issue.
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
Describe a time when you had a technical disagreement with a peer or supervisor. How did you handle it, and what was the outcome?
- 02
Walk me through a complex technical project you led or contributed to significantly. What were the trade-offs you had to make?
- 03
Why do you want to work at Oak St. Health, and how do you see your role contributing to our patient-first mission?
- 04
Tell me about a time you had to quickly learn a new tool or technology to solve an urgent production issue.
How much LeetCode style coding should I expect?
Very little to none. The technical interviews at Oak St. Health focus heavily on real-world engineering skills, such as reading and explaining existing code, manipulating data structures, designing APIs, and discussing system architecture.
Oak St. Health Software Engineer candidate reports ↗What is the typical timeline for the interview process?
The entire process generally takes between two to four weeks from the initial application to the final decision, depending on interviewer availability and how quickly you complete any take-home exercises.
Oak St. Health Software Engineer candidate reports ↗Is this role fully remote, hybrid, or onsite?
Oak St. Health supports hybrid and remote working arrangements depending on the specific team and location, though many engineering teams are centered around offices like the Chicago, IL headquarters. Be sure to clarify expectations with your recruiter during the initial call.
Oak St. Health Software Engineer candidate reports ↗What is the engineering culture like?
The culture is highly collaborative, practical, and mission-driven. Engineers work closely with clinical product owners to build tools that genuinely help people. There is a strong focus on code quality, peer support, and continuous learning rather than working in isolated silos.
Oak St. Health Software Engineer candidate reports ↗What topics does Oak St. Health test in interviews?
Oak St. Health interviews most often cover SQL, Product Discovery, PySpark,.NET, and Collaboration with cross-functional partners. The exact emphasis depends on the specific role you apply for.
Oak St. Health Software Engineer candidate reports ↗What roles can I prepare for at Oak St. Health?
Health, including Account Executive, Business Analyst, Data Analyst, and Data Engineer, and more.
Oak St. Health 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.
- 01Oak St. Health 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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