allwhere Software Engineer Interview Guide

The 60-second brief

allwhere hires Software Engineers; this guide collects what candidates report about the process.

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

Focus areas

• Full-Stack Development
• Web Application Development
• System Design

The allwhere interview process

allwhere candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial conversation with a recruiter to discuss your background and assess fit for the role.

What to demonstrate

• Initial conversation with a recruiter to discuss your background and assess fit for the role
• 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.

Technical Screens

Practical coding interviews focusing on real-world engineering scenarios and your technical skills.

What to demonstrate

• Practical coding interviews focusing on real-world engineering scenarios and your technical skills
• Depth in Full-Stack Development

How to prepare

• Answer aloud and timed: Walk me through the database schema you would design for an asset tracking system that records the full lifecycle of a laptop.
• Answer aloud and timed: How do you ensure high availability and fault tolerance in a microservices architecture?

Onsite Interviews

Multiple rounds of interviews including system design discussions and behavioral assessments.

What to demonstrate

• Multiple rounds of interviews including system design discussions and behavioral assessments
• Depth in Full-Stack Development

How to prepare

• Answer aloud and timed: Build a React component that allows an admin to filter and sort a list of 10,000 employee devices.
• Answer aloud and timed: Write a Node.js API endpoint that safely processes a bulk upload of equipment requests and handles partial failures.

Final Leadership Interviews

Conversations with engineering leaders to ensure mutual fit and alignment with company values.

What to demonstrate

• Conversations with engineering leaders to ensure mutual fit and alignment with company values
• Depth in Full-Stack Development

How to prepare

• Answer aloud and timed: Given this poorly performing SQL query, how would you optimize it to reduce load times on our main dashboard?
• Answer aloud and timed: Implement a rate-limiter for an API to prevent abuse from external clients.

Offer Discussion

Discussion of the job offer details, including salary and benefits.

What to demonstrate

• Discussion of the job offer details
• Including salary and benefits

How to prepare

• Answer aloud and timed: Tell me about a time you had to pivot your technical approach because of shifting business requirements.
• Answer aloud and timed: Describe a situation where you identified a significant technical debt issue. How did you convince leadership to prioritize fixing it?

Where candidates lose points

Think out loud

During coding and design rounds, your thought process is just as important as the final solution. Narrate your approach, explain your trade-offs, and treat the interviewer as a collaborative partner.

Always clarify requirements before writing a single line of code or drawing a system diagram

Asking about scale, expected read/write ratios, and user personas shows maturity and product sense.

Master your tools

You will likely be coding in your own environment or a standard web IDE. Ensure you are completely comfortable with your setup, shortcuts, and debugging tools so you don't waste precious interview time on logistics.

Structure your behavioral answers

Use the STAR (Situation, Task, Action, Result) method. Be specific about your individual contributions ("I built" rather than "We built") and always highlight the business impact of your work.

Questions & practice

Build a React component that allows an admin to filter and sort a list of 10,000 employee devices.

Medium · Full Stack Coding & Execution

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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?

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach

1. Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
2. Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
3. Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
4. Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.

Follow-up

• The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
• The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

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

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

Walk me through the database schema you would design for an asset tracking system that records the full lifecy

Medium · System Design & Architecture

Walk me through the database schema you would design for an asset tracking system that records the full lifecycle of a laptop.

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

Given this poorly performing SQL query, how would you optimize it to reduce load times on our main dashboard?

Medium · Full Stack Coding & Execution

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

Design a webhook ingestion service that handles status updates from our global shipping partners.

Medium · System Design & Architecture

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?

How would you architect a system to sync and merge employee records from multiple different HRIS providers?

Medium · System Design & Architecture

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?

How do you ensure high availability and fault tolerance in a microservices architecture?

Medium · System Design & Architecture

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?

Write a Node.js API endpoint that safely processes a bulk upload of equipment requests and handles partial fai

Medium · Full Stack Coding & Execution

Write a Node.js API endpoint that safely processes a bulk upload of equipment requests and handles partial failures.

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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?

Implement a rate-limiter for an API to prevent abuse from external clients.

Medium · Full Stack Coding & Execution

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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?

p99 jumped on one listing filter while p50 stayed flat

Medium · query planning · composite index · percentiles

After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.

Approach

1. Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
2. Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
3. Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
4. Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.

Follow-up

• The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
• How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?

Suggested practice checklist

Built from the rounds and topics allwhere candidates report.

Day 1 — Map the allwhere loop

• Write out the reported sequence: Recruiter Screen, Technical Screens, Onsite Interviews, Final Leadership Interviews, Offer 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 5 reported rounds, with the weakest marked.

Day 2 — Work Full-Stack Development

• Spend the session on Full-Stack Development, which allwhere 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 Web Application Development

• Spend the session on Web Application Development, which allwhere candidates report being tested on.
• Write one worked example in Web Application Development and time yourself on it.

Deliverable: One timed worked example in Web Application Development.

Day 4 — Work System Design

• Spend the session on System Design, which allwhere 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.

Day 5 — Answer out loud: System Design & Architecture

• Answer aloud, timed: Design a webhook ingestion service that handles status updates from our global shipping partners.
• Answer aloud, timed: How would you architect a system to sync and merge employee records from multiple different HRIS providers?

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

Day 6 — Answer out loud: Full Stack Coding & Execution

• Answer aloud, timed: Build a React component that allows an admin to filter and sort a list of 10,000 employee devices.
• Answer aloud, timed: Write a Node.js API endpoint that safely processes a bulk upload of equipment requests and handles partial failures.

Deliverable: Spoken answers to 2 reported Full Stack Coding & Execution question(s), under time.

Day 7 — Answer out loud: Behavioral & Product Alignment

• Answer aloud, timed: Tell me about a time you had to pivot your technical approach because of shifting business requirements.
• Answer aloud, timed: Describe a situation where you identified a significant technical debt issue. How did you convince leadership to prioritize fixing it?

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

Behavioural preparation

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

Tell me about a time you had to pivot your technical approach because of shifting business requirements.

Medium · Behavioral & Product Alignment

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 identified a significant technical debt issue. How did you convince leadership

Medium · Behavioral & Product Alignment

Describe a situation where you identified a significant technical debt issue. How did you convince leadership to prioritize fixing it?

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 mentored a teammate through a particularly difficult technical challenge.

Medium · Behavioral & Product Alignment

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 balance the need to ship features quickly with the need to write robust, fully tested code?

Medium · Behavioral & Product Alignment

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?