monday Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at monday, you are not just writing code; you are building the connective tissue for how organizations worldwide operate. You will work on a highly dynamic, product-led platform where engineering decisions directly influence the user experience for millions. Whether you are optimizing real-time collaboration, scaling infrastructure to support massive concurrent usage, or developing intuitive UI features, your work has an immediate, tangible impact on how teams get things done. The environment is fast-paced, collaborative, and deeply product-oriented. You will find that monday values engineers who can bridge the gap between complex technical architecture and seamless user features.

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

Focus areas

• Coding Interviews
• System Design
• End-to-End (E2E) Feature Design

The monday interview process

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

Recruiter Screen

Initial screening with a recruiter to assess candidate qualifications and fit.

What to demonstrate

• Initial screening with a recruiter to assess candidate qualifications and fit
• Depth in Coding Interviews

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 Interviews

A series of technical interviews including live coding, feature design, and system architecture.

What to demonstrate

• A series of technical interviews including live coding, feature design, and system architecture
• Depth in Coding Interviews

How to prepare

• Answer aloud and timed: Write code to handle user-to-group mappings to ensure notifications are sent to the right recipients once.
• Answer aloud and timed: How do you handle edge cases when updating large datasets in a real-time environment?

Management Interview

Interview with management focusing on cultural fit and long-term potential.

What to demonstrate

• Interview with management focusing on cultural fit and long-term potential
• Depth in Coding Interviews

How to prepare

• Answer aloud and timed: Given a list of users and their groups, how would you optimize an "inversed mapping" for notification efficiency?
• Answer aloud and timed: Design the architecture for an automation system that triggers actions based on board events.

HR Interview

Final interview with HR to discuss company values and candidate alignment.

What to demonstrate

• Final interview with HR to discuss company values and candidate alignment
• Depth in Coding Interviews

How to prepare

• Answer aloud and timed: How would you design a micro-frontend architecture for a complex dashboard?
• Answer aloud and timed: Discuss the database schema and API design for a high-traffic collaborative feature.

Where candidates lose points

Think out loud

Interviewers care more about your thought process than the final answer. If you get stuck, explain why and ask for their input.

Prepare for feedback

You will likely be asked to discuss past failures or disagreements. Be honest, focus on what you learned, and emphasize your growth.

Ask meaningful questions

At the end of every round, have thoughtful questions about the team’s current challenges, the engineering culture, or the product roadmap.

Manage your time

If you are given a coding task, don't spend too much time on the "naive" solution. Get it working, then pivot quickly to discussing optimizations.

Questions & practice

Implement a function to simulate push notifications for team members while preventing duplicate messages.

Medium · Technical Implementation & Coding

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?

How would you efficiently manage and store the ordering of items in a drag-and-drop board interface?

Medium · Technical Implementation & Coding

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?

Write code to handle user-to-group mappings to ensure notifications are sent to the right recipients once.

Medium · Technical Implementation & Coding

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?

Given a list of users and their groups, how would you optimize an "inversed mapping" for notification efficien

Medium · Technical Implementation & Coding

Given a list of users and their groups, how would you optimize an "inversed mapping" for notification efficiency?

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?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

• The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
• How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

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

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

Design the architecture for an automation system that triggers actions based on board events.

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 design a micro-frontend architecture for a complex dashboard?

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?

Discuss the database schema and API design for a high-traffic collaborative feature.

Medium · System Design & Architecture

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?

How do you ensure data consistency when multiple users interact with the same board simultaneously?

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?

One customer endpoint stalls deliveries to every other destination

Medium · bulkhead · timeouts · saturation

The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.

Approach

1. Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
2. Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
3. Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
4. Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.

Follow-up

• The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
• Where should the 10-second timeout number actually come from?

Suggested practice checklist

Built from the rounds and topics monday candidates report.

Day 1 — Map the monday loop

• Write out the reported sequence: Recruiter Screen, Technical Interviews, Management Interview, HR Interview.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work Coding Interviews

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

Deliverable: One timed worked example in Coding Interviews.

Day 3 — Work System Design

• Spend the session on System Design, which monday 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 4 — Work End-to-End (E2E) Feature Design

• Spend the session on End-to-End (E2E) Feature Design, which monday candidates report being tested on.
• Write one worked example in End-to-End (E2E) Feature Design and time yourself on it.

Deliverable: One timed worked example in End-to-End (E2E) Feature Design.

Day 5 — Answer out loud: Technical Implementation & Coding

• Answer aloud, timed: Implement a function to simulate push notifications for team members while preventing duplicate messages.
• Answer aloud, timed: How would you efficiently manage and store the ordering of items in a drag-and-drop board interface?

Deliverable: Spoken answers to 2 reported Technical Implementation & Coding question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

• Answer aloud, timed: Design the architecture for an automation system that triggers actions based on board events.
• Answer aloud, timed: How would you design a micro-frontend architecture for a complex dashboard?

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

Day 7 — Answer out loud: Behavioral & Culture

• Answer aloud, timed: Describe a time you had a technical disagreement with a teammate; how did you resolve it?
• Answer aloud, timed: Tell me about a project where you had to make a difficult trade-off between speed and quality.

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

Behavioural preparation

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

How do you handle edge cases when updating large datasets in a real-time environment?

Medium · Technical Implementation & Coding

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 time you had a technical disagreement with a teammate; how did you resolve it?

Medium · Behavioral & Culture

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 project where you had to make a difficult trade-off between speed and quality.

Medium · Behavioral & Culture

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 feedback from peers, and can you provide an example of how you’ve implemented it?

Medium · Behavioral & Culture

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?