As a Software Engineer within the Core Platform team at Pave, you are the architect of the infrastructure that powers the industry’s leading compensation platform. You will build the foundational services, APIs, and abstractions that allow our product teams to scale with speed and confidence. Your work directly impacts how 8,500+ companies—including industry leaders like OpenAI, Stripe, and Databricks—make critical, real-time compensation decisions. This role is inherently high-leverage. You aren't just writing features; you are managing critical infrastructure, including user authentication, authorization, and the data pipelines that process over $190 billion in compensation spend. The ideal candidate thrives in an environment where they can solve cross-product challenges, drive large-scale technical initiatives, and maintain a high standard for system reliability and performance. ##### Tip The Core Platform team is the backbone of Pave. Focus your preparation on demonstrating strong systems thinking—showing how you build for the future while delivering value today.
Recruiter Screen
reportedInitial screening call with a recruiter to assess your background and fit for the role.
What to demonstrate
- Initial screening call with a recruiter to assess your background and fit for the role
- Depth in Backend Engineering
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 Deep-Dive
reportedIn-depth technical interview with an engineering manager focusing on your technical skills.
What to demonstrate
- In-depth technical interview with an engineering manager focusing on your technical skills
- Depth in Backend Engineering
How to prepare
- Answer aloud and timed: Explain the trade-offs between different database indexing strategies in MySQL.
- Answer aloud and timed: How do you ensure data integrity in a high-throughput Node.js application?
Onsite/Virtual Loop
reportedMulti-stage interview process that may include coding challenges, system design discussions, and behavioral rounds.
What to demonstrate
- Multi-stage interview process that may include coding challenges, system design discussions, and behavioral rounds
- Depth in Backend Engineering
How to prepare
- Answer aloud and timed: Write a function to transform a nested JSON structure into a flattened format for an ETL pipeline.
- Answer aloud and timed: How would you architect a service to handle authentication for thousands of enterprise users?
PracHub editorial advice for the preparation topics above.
Prioritize Maintenance
In your answers, always emphasize maintainability and scalability. Pave values engineers who build solutions that last, not just "quick fixes."
Master the Stack
Even if you haven't used Prisma or TypeScript extensively, review their core patterns and why they are effective for the Pave platform.
Prepare Your Stories
Use the STAR (Situation, Task, Action, Result) method for all behavioral questions. Focus on your specific contributions and the impact your work had on the team or product.
Own the Ambiguity
When given a system design prompt, don't rush to a solution. Ask clarifying questions to narrow the scope and show your process for handling uncertainty.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the trade-offs between different database indexing strategies in MySQL.
Explain the trade-offs between different database indexing strategies in MySQL.
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 a function to transform a nested JSON structure into a flattened format for an ETL pipeline.
Write a function to transform a nested JSON structure into a flattened format for an ETL pipeline.
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?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
How would you design a scalable API for managing compensation data?
How would you design a scalable API for managing compensation data?
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 ensure data integrity in a high-throughput Node.js application?
How do you ensure data integrity in a high-throughput Node.js application?
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 would you architect a service to handle authentication for thousands of enterprise users?
How would you architect a service to handle authentication for thousands of enterprise users?
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?
Design a data pipeline that processes real-time compensation updates from multiple sources.
Design a data pipeline that processes real-time compensation updates from multiple sources.
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 approach choosing between different data storage solutions for a new microservice?
How do you approach choosing between different data storage solutions for a new microservice?
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 situation where you had to refactor a legacy system to improve performance.
Describe a situation where you had to refactor a legacy system to improve performance.
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 would you handle rate-limiting for a public-facing API?
How would you handle rate-limiting for a public-facing 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?
Can you walk through a time you had to debug a complex issue in a distributed system?
Can you walk through a time you had to debug a complex issue in a distributed system?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Pave candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Pave loop
- Write out the reported sequence: Recruiter Screen, Technical Deep-Dive, Onsite/Virtual Loop.
- 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 3 reported rounds, with the weakest marked.
02Work Backend Engineering
- Spend the session on Backend Engineering, which Pave candidates report being tested on.
- Write one worked example in Backend Engineering and time yourself on it.
Deliverable: One timed worked example in Backend Engineering.
03Work TypeScript
- Spend the session on TypeScript, which Pave candidates report being tested on.
- Write one worked example in TypeScript and time yourself on it.
Deliverable: One timed worked example in TypeScript.
04Work Data Infrastructure
- Spend the session on Data Infrastructure, which Pave candidates report being tested on.
- Write one worked example in Data Infrastructure and time yourself on it.
Deliverable: One timed worked example in Data Infrastructure.
05Answer out loud: Technical & Coding Proficiency
- Answer aloud, timed: How would you design a scalable API for managing compensation data?
- Answer aloud, timed: Can you walk through a time you had to debug a complex issue in a distributed system?
Deliverable: Spoken answers to 2 reported Technical & Coding Proficiency question(s), under time.
06Answer out loud: System Design & Architecture
- Answer aloud, timed: How would you architect a service to handle authentication for thousands of enterprise users?
- Answer aloud, timed: Design a data pipeline that processes real-time compensation updates from multiple sources.
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
07Answer out loud: Behavioral & Leadership
- Answer aloud, timed: Tell me about a time you led a project with significant technical ambiguity.
- Answer aloud, timed: How do you handle disagreements with stakeholders regarding technical trade-offs?
Deliverable: Spoken answers to 2 reported Behavioral & Leadership 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 led a project with significant technical ambiguity.
Tell me about a time you led a project with significant technical ambiguity.
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 stakeholders regarding technical trade-offs?
How do you handle disagreements with stakeholders regarding technical trade-offs?
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?
Give an example of a time you had to prioritize speed over perfection. What was the outcome?
Give an example of a time you had to prioritize speed over perfection. 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?
How do you mentor junior engineers while managing your own technical output?
How do you mentor junior engineers while managing your own technical output?
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 received critical feedback on your code or design and how you handled it.
Describe a time you received critical feedback on your code or design and how you handled 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?
- 01
Tell me about a time you led a project with significant technical ambiguity.
- 02
How do you handle disagreements with stakeholders regarding technical trade-offs?
- 03
Give an example of a time you had to prioritize speed over perfection. What was the outcome?
- 04
How do you mentor junior engineers while managing your own technical output?
How difficult are the coding interviews?
The technical portions are generally considered standard but require a solid grasp of TypeScript and data structures. Focus on writing clean, readable, and efficient code rather than just finding the "perfect" algorithm.
Pave Software Engineer candidate reports ↗What is the best way to stand out?
Successful candidates demonstrate a deep understanding of the "why" behind their technical decisions. When discussing past projects, clearly articulate the trade-offs you made and how those decisions benefited the business.
Pave Software Engineer candidate reports ↗How long does the process usually take?
While the company aims for a fast turnaround, candidates report varying timelines. Stay proactive by following up with your recruiter if you haven't heard back within the expected window, but remain patient as scheduling can fluctuate.
Pave Software Engineer candidate reports ↗Is this a remote-friendly role?
The role is based in New York, NY. Ensure you clarify the specific office presence expectations during your initial recruiter screen.
Pave Software Engineer candidate reports ↗What topics does Pave test in interviews?
Pave interviews most often cover Stakeholder Management, Backend Engineering, Engineering Management, TypeScript, and Recruiting process management. The exact emphasis depends on the specific role you apply for.
Pave 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.
- 01Pave 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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