Interview guides · Groupe SEB Ibérica

Groupe SEB Ibérica Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Groupe SEB Ibérica, you are at the intersection of consumer-facing innovation and industrial excellence. Groupe SEB is a global leader in small domestic equipment, and your work directly impacts how millions of users interact with their home environment. You will be responsible for developing, maintaining, and optimizing software solutions that drive product performance, connectivity, and digital integration. This role is critical to the company’s strategic shift toward smarter, more connected products. You will not just be writing code; you will be solving complex engineering challenges—ranging from embedded systems and robotics to enterprise-level software infrastructure. Whether you are working on IoT integration or internal process automation, your contributions influence the reliability and scalability of the Groupe SEB ecosystem.

This guide is scoped to a Software Engineer candidate at Groupe SEB Ibérica.

Focus areas

Java
Data Structures
Algorithms

The Groupe SEB Ibérica interview process

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

Initial Screening

The first step involves an initial screening to assess your fit for the role.

What to demonstrate

The first step involves an initial screening to assess your fit for the role
Depth in Java

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 Assessment

This round focuses on verifying your technical skills through coding assessments or domain-specific problem-solving.

What to demonstrate

This round focuses on verifying your technical skills through coding assessments or domain-specific problem-solving
Depth in Java

How to prepare

Answer aloud and timed: How do you handle concurrency in your software designs?
Answer aloud and timed: Describe a time you had to optimize a piece of code to improve performance.

Behavioral Evaluation

Evaluate your cultural alignment with the team through behavioral questions.

What to demonstrate

Evaluate your cultural alignment with the team through behavioral questions
Depth in Java

How to prepare

Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
Re-read the description of the behavioral evaluation above and write down what you would ask to confirm before it.

Final Decision

The final stage where the decision is made based on all previous assessments.

What to demonstrate

The final stage where the decision is made based on all previous assessments
Depth in Java

How to prepare

Answer aloud and timed: Describe your process for debugging a system failure in a production environment.
Answer aloud and timed: Given a set of hardware constraints, how do you prioritize software features?

Where candidates lose points

Research the Product Line

Spend time looking at the specific products handled by the site or team you are applying to.

Prepare for Behavioral Questions

Do not underestimate the importance of your soft skills; the team wants to work with someone who communicates clearly and resolves conflicts professionally.

Ask Informed Questions

Prepare 3–5 thoughtful questions about the team's current challenges, the development culture, and how they bridge the hardware-software gap.

Be Professional in Follow-ups

Even if you do not receive an immediate response, maintain a professional tone in your communications.

Questions & practice

Explain the core principles of Java and how they handle memory management.

Medium · Technical Competency and Engineering Fun

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
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?

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

Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

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?

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

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.
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.
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.
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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Can you walk me through your approach to implementing efficient data structures for a high-traffic system?

Medium · Technical Competency and Engineering Fun

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 the trade-offs between different architectural patterns in embedded software?

Medium · Technical Competency and Engineering Fun

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you design a software interface for a robotic manipulator?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Given a set of hardware constraints, how do you prioritize software features?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How do you approach the integration of microcontrollers with higher-level application software?

Medium · Problem-Solving and System Design

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

Describe your process for debugging a system failure in a production environment.

Medium · Problem-Solving and System Design

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

What would you look at first, and what would it rule out?
How would you tell a cause from a coincidence here?

Explain how you would troubleshoot a communication latency issue between an appliance and a cloud service.

Medium · Problem-Solving and System Design

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

What would you look at first, and what would it rule out?
How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Groupe SEB Ibérica candidates report.

Day 1 — Map the Groupe SEB Ibérica loop

Write out the reported sequence: Initial Screening, Technical Assessment, Behavioral Evaluation, Final Decision.
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 Java

Spend the session on Java, which Groupe SEB Ibérica candidates report being tested on.
Write one worked example in Java and time yourself on it.

Deliverable: One timed worked example in Java.

Day 3 — Work Data Structures

Spend the session on Data Structures, which Groupe SEB Ibérica candidates report being tested on.
Write one worked example in Data Structures and time yourself on it.

Deliverable: One timed worked example in Data Structures.

Day 4 — Work Algorithms

Spend the session on Algorithms, which Groupe SEB Ibérica candidates report being tested on.
Write one worked example in Algorithms and time yourself on it.

Deliverable: One timed worked example in Algorithms.

Day 5 — Answer out loud: Technical Competency and Engineering Fundamentals

Answer aloud, timed: Explain the core principles of Java and how they handle memory management.
Answer aloud, timed: Can you walk me through your approach to implementing efficient data structures for a high-traffic system?

Deliverable: Spoken answers to 2 reported Technical Competency and Engineering Fundamentals question(s), under time.

Day 6 — Answer out loud: Problem-Solving and System Design

Answer aloud, timed: How would you design a software interface for a robotic manipulator?
Answer aloud, timed: Describe your process for debugging a system failure in a production environment.

Deliverable: Spoken answers to 2 reported Problem-Solving and System Design question(s), under time.

Day 7 — Answer out loud: Behavioral and Cultural Fit

Answer aloud, timed: Why have you chosen to pursue a career with Groupe SEB?
Answer aloud, timed: Tell us about a time you had to explain a complex technical concept to a non-technical stakeholder.

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

Behavioural preparation

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

How do you handle concurrency in your software designs?

Medium · Technical Competency and Engineering Fun

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

Describe a time you had to optimize a piece of code to improve performance.

Medium · Technical Competency and Engineering Fun

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?