cswork / libraryOpen practice workspace

Interview guides · Replo

Replo Software Engineer Interview Guide

Replo Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Replo, a Software Engineer is responsible for building the core infrastructure and user-facing features that power a highly customizable, low-code web building platform. Because Replo enables e-commerce brands and developers to design complex, high-performance web pages without writing boilerplate code, the engineering challenges here are unique. You will not just be building standard web applications; you will be building the engines, visual editors, and rendering systems that generate them. The impact of this role is immediate and highly visible. Your work directly affects how thousands of merchants design, launch, and optimize their online stores. This requires a deep understanding of frontend performance, complex state management, and seamless backend integrations.

This guide is scoped to a Software Engineer candidate at Replo.

Focus areas

  • Full-Stack Development
  • React
  • Take-Home Projects

The Replo interview process

Replo candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Phone Screen

Initial phone screen with a recruiter, which may be recorded and summarized for the hiring team.

What to demonstrate

  • Initial phone screen with a recruiter, which may be recorded and summarized for the hiring team
  • Depth in Full-Stack Development

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.

Take-Home Assignment

Complete a mini full-stack take-home assignment to build a small application using React and a backend framework.

What to demonstrate

  • Complete a mini full-stack take-home assignment to build a small application using React and a backend framework
  • Depth in Full-Stack Development

How to prepare

  • Answer aloud and timed: Walk through your choice of backend technology for a mini full-stack application and explain how you structured the communication layer between the client and the server.
  • Answer aloud and timed: How do you handle asynchronous state updates and race conditions when fetching data from external e-commerce APIs?

Technical Pair-Programming

Participate in a pair-programming round to debug, extend, or add a feature to your take-home codebase with a Replo engineer.

What to demonstrate

  • Participate in a pair-programming round to debug, extend, or add a feature to your take-home codebase with a Replo engineer
  • Depth in Full-Stack Development

How to prepare

  • Answer aloud and timed: Describe your approach to writing reusable, self-documenting components that other engineers on the team can easily extend.
  • Answer aloud and timed: Design a real-time collaborative document editing system similar to Google Docs, focusing on conflict resolution and low-latency updates.

On-Site Loop

Engage in multiple behavioral interviews and a system design session during the final on-site stage.

What to demonstrate

  • Engage in multiple behavioral interviews and a system design session during the final on-site stage
  • Depth in Full-Stack Development

How to prepare

  • Answer aloud and timed: How would you architect a content delivery network (CDN) caching strategy for a platform that serves dynamically generated web pages to millions of end-users?
  • Answer aloud and timed: Describe how you would design a robust third-party integration pipeline that syncs product catalogs from platforms like Shopify in real-time.

Where candidates lose points

Focus on the MVP first

During the take-home and pair-programming sessions, prioritize delivering a working, functional feature before trying to optimize every line of code. It is better to have a complete, clean MVP than a highly optimized system that does not run.

Over-communicate your thoughts

When coding or designing systems live, talk through your thought process. Explain why you are choosing a specific library, framework, or database structure, and openly discuss any trade-offs you are making.

Prepare for e-commerce contexts

Because Replo is heavily integrated with the e-commerce ecosystem, brushing up on e-commerce concepts—such as cart management, product catalogs, dynamic pricing, and checkout flows—can give you a significant advantage.

Questions & practice

How do you handle asynchronous state updates and race conditions when fetching data from external e-commerce A

Medium · Full-Stack & Frontend Engineering

How do you handle asynchronous state updates and race conditions when fetching data from external e-commerce APIs?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

  1. Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
  2. Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
  3. State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
  4. Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

  • The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
  • Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

  1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
  2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
  3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
  4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

  • A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
  • Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

How would you optimize the rendering performance of a highly interactive drag-and-drop user interface in React

Medium · Full-Stack & Frontend Engineering

How would you optimize the rendering performance of a highly interactive drag-and-drop user interface in React?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Explain how you would manage complex, nested state in a collaborative web application where multiple users mig

Medium · Full-Stack & Frontend Engineering

Explain how you would manage complex, nested state in a collaborative web application where multiple users might edit the same page layout.

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Walk through your choice of backend technology for a mini full-stack application and explain how you structure

Medium · Full-Stack & Frontend Engineering

Walk through your choice of backend technology for a mini full-stack application and explain how you structured the communication layer between the client and the server.

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 writing reusable, self-documenting components that other engineers on the team can e

Medium · Full-Stack & Frontend Engineering

Describe your approach to writing reusable, self-documenting components that other engineers on the team can easily extend.

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 architect a content delivery network (CDN) caching strategy for a platform that serves dynamical

Medium · System Design & Architecture

How would you architect a content delivery network (CDN) caching strategy for a platform that serves dynamically generated web pages to millions of end-users?

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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 how you would design a robust third-party integration pipeline that syncs product catalogs from platf

Medium · System Design & Architecture

Describe how you would design a robust third-party integration pipeline that syncs product catalogs from platforms like Shopify in real-time.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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 rate-limiting service to protect internal APIs from abuse while ensuring legitimate user traffic is n

Medium · System Design & Architecture

Design a rate-limiting service to protect internal APIs from abuse while ensuring legitimate user traffic is never disrupted.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

  1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
  2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
  3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
  4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

  • The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
  • How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Replo candidates report.

Day 1 — Map the Replo loop

  • Write out the reported sequence: Recruiter Phone Screen, Take-Home Assignment, Technical Pair-Programming, On-Site 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 4 reported rounds, with the weakest marked.

Day 2 — Work Full-Stack Development

  • Spend the session on Full-Stack Development, which Replo candidates report being tested on.
  • Write one worked example in Full-Stack Development and time yourself on it.

Deliverable: One timed worked example in Full-Stack Development.

Day 3 — Work React

  • Spend the session on React, which Replo candidates report being tested on.
  • Write one worked example in React and time yourself on it.

Deliverable: One timed worked example in React.

Day 4 — Work Take-Home Projects

  • Spend the session on Take-Home Projects, which Replo candidates report being tested on.
  • Write one worked example in Take-Home Projects and time yourself on it.

Deliverable: One timed worked example in Take-Home Projects.

Day 5 — Answer out loud: Full-Stack & Frontend Engineering

  • Answer aloud, timed: How would you optimize the rendering performance of a highly interactive drag-and-drop user interface in React?
  • Answer aloud, timed: Explain how you would manage complex, nested state in a collaborative web application where multiple users might edit the same page layout.

Deliverable: Spoken answers to 2 reported Full-Stack & Frontend Engineering question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: Design a real-time collaborative document editing system similar to Google Docs, focusing on conflict resolution and low-latency updates.
  • Answer aloud, timed: How would you architect a content delivery network (CDN) caching strategy for a platform that serves dynamically generated web pages to millions of end-users?

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral & Collaboration

  • Answer aloud, timed: Tell me about a time you had to deliver a critical feature under a tight deadline. How did you manage technical debt and scope creep?
  • Answer aloud, timed: Describe a situation where you disagreed with a teammate or stakeholder on a technical decision. How did you resolve the conflict and move forward?

Deliverable: Spoken answers to 2 reported Behavioral & Collaboration question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Design a real-time collaborative document editing system similar to Google Docs, focusing on conflict resoluti

Medium · System Design & Architecture

Design a real-time collaborative document editing system similar to Google Docs, focusing on conflict resolution and low-latency updates.

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 schema migrations for a database that supports highly dynamic, user-defined data structures?

Medium · System Design & Architecture

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 deliver a critical feature under a tight deadline. How did you manage technica

Medium · Behavioral & Collaboration

Tell me about a time you had to deliver a critical feature under a tight deadline. How did you manage technical debt and scope creep?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 situation where you disagreed with a teammate or stakeholder on a technical decision. How did you r

Medium · Behavioral & Collaboration

Describe a situation where you disagreed with a teammate or stakeholder on a technical decision. How did you resolve the conflict and move forward?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 prioritize your work when faced with multiple high-priority bugs and feature requests?

Medium · Behavioral & Collaboration

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 received constructive feedback on your code that you initially disagreed with. How di

Medium · Behavioral & Collaboration

Tell me about a time you received constructive feedback on your code that you initially disagreed with. How did you handle the conversation?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 work with an ambiguous product requirement. How did you define the scope

Medium · Behavioral & Collaboration

Give an example of a time you had to work with an ambiguous product requirement. How did you define the scope and deliver the final product?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?