Interview guides · IT Partner España

IT Partner España Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at IT Partner España, you are a core driver of our technical infrastructure and digital transformation initiatives. This role is pivotal in bridging complex business requirements with scalable software solutions, directly impacting the robustness and efficiency of the services we provide to our clients. You will not just be writing code; you will be architecting systems, optimizing performance, and ensuring that our technical standards remain world-class. The environment at IT Partner España is fast-paced and collaborative. You will frequently work alongside cross-functional teams, including product managers, architects, and quality assurance engineers, to solve real-world problems. Whether you are working on backend optimization, API integration, or front-end interface development, your contributions will have a tangible effect on the end-user experience.

This guide is scoped to a Software Engineer candidate at IT Partner España.

Focus areas

Java
Object-Oriented Programming (OOP)
Database Fundamentals

The IT Partner España interview process

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

Initial Screening

Gauge your background and interest in the position.

What to demonstrate

Gauge your background and interest in the position
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 Discussions

Engage in a series of technical discussions to assess your skills.

What to demonstrate

Engage in a series of technical discussions to assess your skills
Depth in Java

How to prepare

Answer aloud and timed: How do you optimize the performance of a website or application?
Answer aloud and timed: Can you explain the core concepts of OOP (Object-Oriented Programming) and how you implement them in Java?

Practical Assessment

In some cases, complete a practical assessment or case study presentation.

What to demonstrate

In some cases, complete a practical assessment or case study presentation
Depth in Java

How to prepare

Answer aloud and timed: What are the key differences between SQL and NoSQL databases, and when would you choose one over the other?
Answer aloud and timed: Can you walk us through a recent project you worked on and the specific challenges you faced?

Managerial Discussions

Final discussions with management to evaluate overall fit.

What to demonstrate

Final discussions with management to evaluate overall fit
Depth in Java

How to prepare

Answer aloud and timed: Why are you interested in joining IT Partner España at this stage in your career?
Answer aloud and timed: Tell us about a time you had to resolve a conflict within your team.

Where candidates lose points

Structure your answers

Use the STAR method (Situation, Task, Action, Result) when answering behavioral questions to keep your responses concise and impactful.

Ask thoughtful questions

At the end of your interviews, ask about the team’s current challenges or the company’s technical direction to show your engagement.

Be honest about your limits

If you encounter a question on a technology you don't know, explain your approach to learning new tools rather than guessing.

Always research our recent technical initiatives

Being aware of our broader business goals demonstrates that you are a serious and prepared candidate.

Questions & practice

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?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

What are the key differences between SQL and NoSQL databases, and when would you choose one over the other?

Medium · Technical Fundamentals

Approach

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

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

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.
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.
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.
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?

How do you apply the DRY (Don't Repeat Yourself) principle in your daily coding?

Medium · Technical Fundamentals

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 the difference between FDD and TDD in network and software integration?

Medium · Technical Fundamentals

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?

How do you optimize the performance of a website or application?

Medium · Technical Fundamentals

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 the core concepts of OOP (Object-Oriented Programming) and how you implement them in Java?

Medium · Technical Fundamentals

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?

Describe how you would design a system to handle high traffic and data volume.

Medium · Problem-Solving and Case Studies

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?

If you were tasked with building a new feature using Generative AI, how would you approach the design and impl

Medium · Problem-Solving and Case Studies

If you were tasked with building a new feature using Generative AI, how would you approach the design and implementation?

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 troubleshoot a complex issue within a distributed system?

Medium · Problem-Solving and Case Studies

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?

Walk us through your process for debugging a production-level issue.

Medium · Problem-Solving and Case Studies

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 IT Partner España candidates report.

Day 1 — Map the IT Partner España loop

Write out the reported sequence: Initial Screening, Technical Discussions, Practical Assessment, Managerial Discussions.
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 IT Partner España 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 Object-Oriented Programming (OOP)

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

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

Day 4 — Work Database Fundamentals

Spend the session on Database Fundamentals, which IT Partner España candidates report being tested on.
Write one worked example in Database Fundamentals and time yourself on it.

Deliverable: One timed worked example in Database Fundamentals.

Day 5 — Answer out loud: Technical Fundamentals

Answer aloud, timed: How do you apply the DRY (Don't Repeat Yourself) principle in your daily coding?
Answer aloud, timed: Can you explain the difference between FDD and TDD in network and software integration?

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

Day 6 — Answer out loud: Behavioral and Career Aspirations

Answer aloud, timed: Can you walk us through a recent project you worked on and the specific challenges you faced?
Answer aloud, timed: Why are you interested in joining IT Partner España at this stage in your career?

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

Day 7 — Answer out loud: Problem-Solving and Case Studies

Answer aloud, timed: Describe how you would design a system to handle high traffic and data volume.
Answer aloud, timed: How would you troubleshoot a complex issue within a distributed system?

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