As a Software Engineer at Highnote, you are joining a mission-critical team building the modern infrastructure for card issuance and payments. This role is not about maintaining legacy systems; it is about architecting scalable, highly available solutions that handle complex, real-world financial transactions. You will be at the forefront of the fintech revolution, working on challenges that directly impact how businesses issue, manage, and process payments. Your work will involve deep technical collaboration across the engineering organization, often interfacing with infrastructure, product, and leadership teams. Whether you are optimizing multi-region architectures, implementing robust APIs, or ensuring the reliability of a high-volume credit card processor, your contributions will be central to the company’s growth. You will face problems that require both precision in execution and a high-level understanding of system design, making this an ideal environment for engineers who thrive on technical depth and real-world impact. ##### Tip Highnote values "practicality over theory." While technical rigor is essential, prioritize getting a working solution on the board before optimizing for edge cases or over-engineering your code.
Recruiter Screen
reportedInitial screening call with a recruiter to discuss the candidate's background and role fit.
What to demonstrate
- Initial screening call with a recruiter to discuss the candidate's background and role fit
- Depth in Coding Interviews (General)
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 Coding Round
reportedTechnical assessment that simulates real-world coding scenarios.
What to demonstrate
- Technical assessment that simulates real-world coding scenarios
- Depth in Coding Interviews (General)
How to prepare
- Answer aloud and timed: Solve a real-world coding challenge related to the Highnote platform.
- Answer aloud and timed: Write code to handle specific data transformation tasks.
Architecture Round
reportedAssessment focused on architectural concepts and design.
What to demonstrate
- Assessment focused on architectural concepts and design
- Depth in Coding Interviews (General)
How to prepare
- Answer aloud and timed: Complete a pair-programming exercise focused on following new documentation to build a component.
- Answer aloud and timed: Design a highly available, multi-region architecture.
Behavioral Discussion
reportedConversations with engineering leadership and the CTO to assess cultural fit.
What to demonstrate
- Conversations with engineering leadership and the CTO to assess cultural fit
- Depth in Coding Interviews (General)
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral discussion above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Prioritize clarity
When explaining your design choices, walk the interviewer through your thought process clearly. They care as much about how you think as what you end up with.
Be ready for the "Ethos" interview
This is a core part of the culture. Be prepared to talk about why you want to work at Highnote specifically, not just why you want a new job.
Ask thoughtful questions
Use your time with the CTO or Team Leads to ask about the company’s long-term technical roadmap. It shows you are thinking about the business, not just your specific code.
Own your mistakes
If you realize you made a mistake during a coding round, communicate it immediately and explain how you would fix it. This shows professional maturity.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Build a feature for an existing React and TypeScript application.
Build a feature for an existing React and TypeScript application.
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?
Implement a functional credit card processor logic.
Implement a functional credit card processor logic.
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?
Solve a real-world coding challenge related to the Highnote platform.
Solve a real-world coding challenge related to the Highnote platform.
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?
Write code to handle specific data transformation tasks.
Write code to handle specific data transformation tasks.
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?
Complete a pair-programming exercise focused on following new documentation to build a component.
Complete a pair-programming exercise focused on following new documentation to build a component.
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?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
Design a highly available, multi-region architecture.
Design a highly available, multi-region 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?
Explain how you would handle data consistency in a distributed banking system.
Explain how you would handle data consistency in a distributed banking 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?
Discuss the trade-offs between different API design patterns.
Discuss the trade-offs between different API design patterns.
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 would you structure a system to handle high-volume transaction traffic?
How would you structure a system to handle high-volume transaction traffic?
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 a challenging architectural decision you made in a previous project.
Describe a challenging architectural decision you made in a previous project.
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?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics Highnote candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Highnote loop
- Write out the reported sequence: Recruiter Screen, Technical Coding Round, Architecture Round, Behavioral Discussion.
- 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 Coding Interviews (General)
- Spend the session on Coding Interviews (General), which Highnote candidates report being tested on.
- Write one worked example in Coding Interviews (General) and time yourself on it.
Deliverable: One timed worked example in Coding Interviews (General).
03Work Problem Solving
- Spend the session on Problem Solving, which Highnote candidates report being tested on.
- Write one worked example in Problem Solving and time yourself on it.
Deliverable: One timed worked example in Problem Solving.
04Work System Design
- Spend the session on System Design, which Highnote candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
05Answer out loud: Coding and Implementation
- Answer aloud, timed: Build a feature for an existing React and TypeScript application.
- Answer aloud, timed: Implement a functional credit card processor logic.
Deliverable: Spoken answers to 2 reported Coding and Implementation question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Design a highly available, multi-region architecture.
- Answer aloud, timed: Explain how you would handle data consistency in a distributed banking system.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and "Ethos"
- Answer aloud, timed: Tell me about a time you had to learn a new technology stack quickly to solve a problem.
- Answer aloud, timed: How do you handle disagreements with team leads regarding technical direction?
Deliverable: Spoken answers to 2 reported Behavioral and "Ethos" 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.
Tell me about a time you had to learn a new technology stack quickly to solve a problem.
Tell me about a time you had to learn a new technology stack quickly to solve a problem.
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 disagreements with team leads regarding technical direction?
How do you handle disagreements with team leads regarding technical direction?
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 your process for working cross-functionally with product managers.
Describe your process for working cross-functionally with product managers.
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?
What motivates you to solve complex problems in the fintech space?
What motivates you to solve complex problems in the fintech space?
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 project where you had to balance technical debt with shipping a new feature.
Tell me about a project where you had to balance technical debt with shipping a new feature.
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
Tell me about a time you had to learn a new technology stack quickly to solve a problem.
- 02
How do you handle disagreements with team leads regarding technical direction?
- 03
Describe your process for working cross-functionally with product managers.
- 04
What motivates you to solve complex problems in the fintech space?
How difficult are the coding rounds?
They are generally considered "average" in difficulty. They are less about "LeetCode" style puzzles and more about practical, day-to-day engineering tasks.
Highnote Software Engineer candidate reports ↗What is the best way to prepare for the architecture round?
Focus on "real-world" scalability. Think about how you would design a system that cannot afford downtime and needs to be globally consistent.
Highnote Software Engineer candidate reports ↗Is the process purely remote?
Highnote has a strong presence in San Francisco, but many roles are open to candidates across the United States. Confirm the specific location expectations for your role with your recruiter.
Highnote Software Engineer candidate reports ↗How long does the entire process take?
It is often quite efficient, typically ranging from two to four weeks depending on scheduling.
Highnote Software Engineer candidate reports ↗What topics does Highnote test in interviews?
Highnote interviews most often cover Problem Solving, Coding Interviews (General), Risk Analysis, Risk Identification, and System Design. The exact emphasis depends on the specific role you apply for.
Highnote 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.
- 01Highnote 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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