Lloyd’s / London Market Insurance Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Lloyd’s / London Market Insurance, you are at the intersection of high-stakes global finance and cutting-edge technical innovation. The London Market is a complex, data-driven ecosystem, and your work directly impacts the stability and efficiency of insurance operations that support businesses and economies worldwide. You will be tasked with building and maintaining resilient systems that handle critical financial transactions, risk modeling, and complex data processing. Your role is not merely about writing code; it is about architectural stewardship. You will contribute to the modernization of legacy frameworks while integrating modern Microservices and Spring Boot architectures.

This guide is scoped to a Software Engineer candidate at Lloyd’s / London Market Insurance.

Focus areas

• Java
• HashMap Internals
• Spring Boot

The Lloyd’s / London Market Insurance interview process

Lloyd’s / London Market Insurance candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial screening conducted by a recruiter to assess your fit for the role.

What to demonstrate

• Initial screening conducted by a recruiter 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 Interviews

Deep-dive technical interviews with engineering leads focusing on practical application.

What to demonstrate

• Deep-dive technical interviews with engineering leads focusing on practical application
• Depth in Java

How to prepare

• Answer aloud and timed: Provide the output for programs involving static method overriding.
• Answer aloud and timed: How does the load factor affect the performance of a HashMap?

Theoretical Knowledge

Sessions that assess your theoretical knowledge relevant to the role.

What to demonstrate

• Sessions that assess your theoretical knowledge relevant to the role
• Depth in Java

How to prepare

• Answer aloud and timed: Discuss the time complexity for insertion and retrieval in various collections.
• Answer aloud and timed: Design an algorithm to find the longest palindrome within a given string.

Live Coding

Hands-on coding sessions to evaluate your coding skills in real-time.

What to demonstrate

• Hands-on coding sessions to evaluate your coding skills in real-time
• Depth in Java

How to prepare

• Answer aloud and timed: Use the Java Streams API to identify the second shortest element in a collection.
• Answer aloud and timed: How do you handle edge cases when manipulating strings or large datasets?

Architectural Discussions

Discussions focused on architectural design patterns and system design.

What to demonstrate

• Discussions focused on architectural design patterns and system design
• Depth in Java

How to prepare

• Answer aloud and timed: What are the key advantages and challenges of transitioning to a Microservices architecture?
• Answer aloud and timed: Explain your approach to RESTful API design and documentation.

Where candidates lose points

Practice live coding

Use an IDE or a simple text editor to practice writing clean, efficient code without relying on auto-complete tools.

Master the fundamentals

Many candidates focus too much on frameworks and forget the core language features—ensure your Java fundamentals are rock solid.

Review your projects

Be prepared to discuss the architecture of your past projects, specifically why you made certain technical trade-offs.

Think aloud

When solving a problem, communicate your logic. This helps the interviewer understand your problem-solving style, even if you hit a snag.

Questions & practice

Explain the internal working of HashMap, including how it handles hash collisions and resizing.

Medium · Java Proficiency and Core Language Conce

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

What is the difference between static and dynamic method binding in Java?

Medium · Java Proficiency and Core Language Conce

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Provide the output for programs involving static method overriding.

Medium · Java Proficiency and Core Language Conce

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

How does the load factor affect the performance of a HashMap?

Medium · Java Proficiency and Core Language Conce

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Discuss the time complexity for insertion and retrieval in various collections.

Medium · Java Proficiency and Core Language Conce

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?

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

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

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

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

Design an algorithm to find the longest palindrome within a given string.

Medium · Algorithms and Data Structures

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?

Use the Java Streams API to identify the second shortest element in a collection.

Medium · Algorithms and Data Structures

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?

What are the key advantages and challenges of transitioning to a Microservices architecture?

Medium · System Design and 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?

Explain your approach to RESTful API design and documentation.

Medium · System Design and 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 manage dependency injection and inversion of control in Spring Boot?

Medium · System Design and 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?

What strategies do you use for monitoring and scalability in a distributed environment?

Medium · System Design and 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?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

• The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
• How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Lloyd’s / London Market Insurance candidates report.

Day 1 — Map the Lloyd’s / London Market Insurance loop

• Write out the reported sequence: Recruiter Screen, Technical Interviews, Theoretical Knowledge, Live Coding, Architectural 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 5 reported rounds, with the weakest marked.

Day 2 — Work Java

• Spend the session on Java, which Lloyd’s / London Market Insurance 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 HashMap Internals

• Spend the session on HashMap Internals, which Lloyd’s / London Market Insurance candidates report being tested on.
• Write one worked example in HashMap Internals and time yourself on it.

Deliverable: One timed worked example in HashMap Internals.

Day 4 — Work Spring Boot

• Spend the session on Spring Boot, which Lloyd’s / London Market Insurance candidates report being tested on.
• Write one worked example in Spring Boot and time yourself on it.

Deliverable: One timed worked example in Spring Boot.

Day 5 — Answer out loud: Java Proficiency and Core Language Concepts

• Answer aloud, timed: Explain the internal working of HashMap, including how it handles hash collisions and resizing.
• Answer aloud, timed: What is the difference between static and dynamic method binding in Java?

Deliverable: Spoken answers to 2 reported Java Proficiency and Core Language Concepts question(s), under time.

Day 6 — Answer out loud: Algorithms and Data Structures

• Answer aloud, timed: Design an algorithm to find the longest palindrome within a given string.
• Answer aloud, timed: Use the Java Streams API to identify the second shortest element in a collection.

Deliverable: Spoken answers to 2 reported Algorithms and Data Structures question(s), under time.

Day 7 — Answer out loud: System Design and Architecture

• Answer aloud, timed: What are the key advantages and challenges of transitioning to a Microservices architecture?
• Answer aloud, timed: Explain your approach to RESTful API design and documentation.

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

Behavioural preparation

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

How do you handle edge cases when manipulating strings or large datasets?

Medium · Algorithms and Data Structures

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?

Reverse your own decision and price the reversal

Medium · reversibility · measurement · migrations

Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.

Approach

1. State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
2. Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
3. Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
4. Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.

Follow-up

• What in that decision was irreversible, and did you know it was irreversible when you made it?
• How did you tell the people who had already built on top of the original decision?

Argue against a design, lose, and commit anyway

Medium · disagreement · service boundaries · decision records

Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.

Approach

1. State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
2. Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
3. Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
4. Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.

Follow-up

• What threshold on that alert would have proved you right, and did anyone ever look at it?
• If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?