Interview guides · H&M

H&M Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at H&M, you are at the intersection of global-scale retail and cutting-edge technology. You are not just writing code; you are building the digital backbone that powers one of the world's most recognizable fashion brands. Your work influences how millions of customers interact with products, manage their orders, and experience the brand across both physical stores and digital storefronts. This role is critical to H&M’s ongoing digital transformation. You will contribute to complex, high-traffic systems that demand both stability and innovation. Whether you are optimizing backend services, refining UI components, or developing scalable architecture, your contributions directly impact the efficiency of global operations and the quality of the customer journey.

This guide is scoped to a Software Engineer candidate at H&M.

Focus areas

OOP (Object-Oriented Programming)
Coding Interviews (Problem Solving)
Data Structures & Algorithms (DSA)

The H&M interview process

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

Initial Vetting

Candidates may undergo online aptitude and coding assessments.

What to demonstrate

Candidates may undergo online aptitude and coding assessments
Depth in OOP (Object-Oriented Programming)

How to prepare

Answer aloud and timed: How would you explain OOP concepts and provide a practical code example?
Answer aloud and timed: Can you solve coding challenges involving string manipulation, arrays, or loops?

HR Interview

An interview focusing on cultural fit and general background.

What to demonstrate

An interview focusing on cultural fit and general background
Depth in OOP (Object-Oriented Programming)

How to prepare

Answer aloud and timed: What is your experience with Java, JavaScript, React, or Node.js?
Answer aloud and timed: How do you approach the logic for finding the second-largest element in an array?

Technical Interviews

A series of interviews with technical leads assessing technical capabilities.

What to demonstrate

A series of interviews with technical leads assessing technical capabilities
Depth in OOP (Object-Oriented Programming)

How to prepare

Answer aloud and timed: Can you explain how to solve a palindrome string challenge?
Answer aloud and timed: Describe a difficult day at work—what caused it and how did you manage it?

Management Interview

Final interview with management to evaluate overall fit and alignment.

What to demonstrate

Final interview with management to evaluate overall fit and alignment
Depth in OOP (Object-Oriented Programming)

How to prepare

Answer aloud and timed: How do you handle conflict in the workplace?
Answer aloud and timed: What qualities do you value most in a team, and what strengths do you bring?

Where candidates lose points

Prepare for the "Resume Grind

Be prepared to explain every single project and technical choice listed on your resume. If you list a technology, expect to be asked about it in detail.

Practice Your Stories

Use the STAR method (Situation, Task, Action, Result) for behavioral questions. This keeps your answers structured and easy for the interviewer to follow.

Ask Thoughtful Questions

Use your time at the end of the interview to ask about the team's challenges or the company's tech roadmap. This shows engagement and strategic thinking.

Questions & practice

How do you approach the logic for finding the second-largest element in an array?

Medium · Technical and Domain Proficiency

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?

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

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.
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.
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.
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?

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

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.
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.
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.
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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

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

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?

How would you explain OOP concepts and provide a practical code example?

Medium · Technical and Domain Proficiency

Approach

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

Follow-up

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

Can you solve coding challenges involving string manipulation, arrays, or loops?

Medium · Technical and Domain Proficiency

Approach

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

Follow-up

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

Can you explain how to solve a palindrome string challenge?

Medium · Technical and Domain Proficiency

Approach

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

Follow-up

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

Why are you the right person for this specific role?

Medium · Situational and Strategy

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
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 do you maintain your energy and focus during high-pressure periods?

Medium · Situational and Strategy

Approach

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

Follow-up

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

What are your thoughts on current technology trends and their impact on retail?

Medium · Situational and Strategy

Approach

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

Follow-up

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

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics H&M candidates report.

Day 1 — Map the H&M loop

Write out the reported sequence: Initial Vetting, HR Interview, Technical Interviews, Management 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 OOP (Object-Oriented Programming)

Spend the session on OOP (Object-Oriented Programming), which H&M candidates report being tested on.
Write one worked example in OOP (Object-Oriented Programming) and time yourself on it.

Deliverable: One timed worked example in OOP (Object-Oriented Programming).

Day 3 — Work Coding Interviews (Problem Solving)

Spend the session on Coding Interviews (Problem Solving), which H&M candidates report being tested on.
Write one worked example in Coding Interviews (Problem Solving) and time yourself on it.

Deliverable: One timed worked example in Coding Interviews (Problem Solving).

Day 4 — Work Data Structures & Algorithms (DSA)

Spend the session on Data Structures & Algorithms (DSA), which H&M candidates report being tested on.
Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 5 — Answer out loud: Technical and Domain Proficiency

Answer aloud, timed: How would you explain OOP concepts and provide a practical code example?
Answer aloud, timed: Can you solve coding challenges involving string manipulation, arrays, or loops?

Deliverable: Spoken answers to 2 reported Technical and Domain Proficiency question(s), under time.

Day 6 — Answer out loud: Behavioral and Leadership

Answer aloud, timed: Describe a difficult day at work—what caused it and how did you manage it?
Answer aloud, timed: How do you handle conflict in the workplace?

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

Day 7 — Answer out loud: Situational and Strategy

Answer aloud, timed: Why are you the right person for this specific role?
Answer aloud, timed: How do you maintain your energy and focus during high-pressure periods?

Deliverable: Spoken answers to 2 reported Situational and Strategy question(s), under time.

Behavioural preparation

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

What is your experience with Java, JavaScript, React, or Node.js?

Medium · Technical and Domain Proficiency

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?