Interview guides · Crum & Forster

Crum & Forster Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at Crum & Forster plays a pivotal role in driving the digital transformation of a premier specialty insurance market leader. Operating within a highly collaborative ecosystem, engineers in this role are responsible for building, optimizing, and maintaining the core digital solutions that power underwriting, claims processing, and broker interactions. By developing scalable full-stack applications and high-performance APIs, you directly influence the efficiency of business operations and the overall user experience for internal teams and external partners. The work is both technically challenging and strategically significant. You will be tasked with modernizing legacy insurance systems and constructing next-generation platforms using a modern technology stack that includes C#.NET Core, GraphQL, React, and Next.js.

This guide is scoped to a Software Engineer candidate at Crum & Forster.

Focus areas

C# (.NET)
iSeries / AS400 Development
GraphQL

The Crum & Forster interview process

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

Recruiter Phone Screen

Initial call to discuss your background, career aspirations, and salary alignment.

What to demonstrate

Initial call to discuss your background, career aspirations, and salary alignment
Depth in C# (.NET)

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.

Hiring Manager Interview

Conversational interview focusing on your past project experiences.

What to demonstrate

Conversational interview focusing on your past project experiences
Depth in C# (.NET)

How to prepare

Prepare two projects you led end to end, each with the decision you owned and what it cost.
Have three questions about the team's roadmap and how success is measured in the first six months.

Technical Evaluations

Includes take-home presentations, live problem-solving tasks, or technical and behavioral panels.

What to demonstrate

Includes take-home presentations, live problem-solving tasks, or technical and behavioral panels
Depth in C# (.NET)

How to prepare

Answer aloud and timed: How do you ensure your API designs remain backward-compatible as business requirements evolve?
Answer aloud and timed: Given a specific take-home technical topic, how would you research, structure, and present your findings to a panel of senior engineers?

Onsite Interview Day

Comprehensive interview day at the office with multiple sessions and a lunch meeting.

What to demonstrate

Comprehensive interview day at the office with multiple sessions and a lunch meeting
Depth in C# (.NET)

How to prepare

Answer aloud and timed: Walk us through a live coding or system design scenario where you have to resolve a data synchronization conflict between two microservices.
Answer aloud and timed: How would you design a scalable, fault-tolerant file upload and processing system for high-volume insurance documentation?

Where candidates lose points

Master the Core Stack

Ensure you are highly fluent in C#.NET Core, React, and GraphQL. Be ready to discuss concrete examples of how you have used these technologies to solve real-world engineering challenges in your past roles.

Be prepared to discuss your past projects in granular detail

Interviewers will ask you to explain the "why" behind your architectural choices, database schemas, and API designs.

Understand the Business Context

Spend time researching the commercial insurance industry and Crum & Forster's business model. Showing that you understand how software engineering drives underwriting efficiency and business growth will set you apart from other candidates.

Questions & practice

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?

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?

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?

Explain how you would optimize a slow-performing database query in a C#.NET Core application.

Medium · Technical & Architecture Deep-Dive

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?

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?

What are the primary advantages of using GraphQL over traditional REST APIs in a high-traffic web application?

Medium · Technical & Architecture Deep-Dive

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 manage state and optimize rendering performance in a complex React or Next.js application?

Medium · Technical & Architecture Deep-Dive

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?

Describe your approach to handling network requirements, data security, and encryption protocols for enterpris

Medium · Technical & Architecture Deep-Dive

Describe your approach to handling network requirements, data security, and encryption protocols for enterprise-level applications.

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 ensure your API designs remain backward-compatible as business requirements evolve?

Medium · Technical & Architecture Deep-Dive

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?

Given a specific take-home technical topic, how would you research, structure, and present your findings to a

Medium · System Design & Problem-Solving

Given a specific take-home technical topic, how would you research, structure, and present your findings to a panel of senior engineers?

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?

Walk us through a live coding or system design scenario where you have to resolve a data synchronization confl

Medium · System Design & Problem-Solving

Walk us through a live coding or system design scenario where you have to resolve a data synchronization conflict between two microservices.

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 scalable, fault-tolerant file upload and processing system for high-volume insurance do

Medium · System Design & Problem-Solving

How would you design a scalable, fault-tolerant file upload and processing system for high-volume insurance documentation?

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?

What architectural patterns do you rely on to separate business logic from presentation layers in a full-stack

Medium · System Design & Problem-Solving

What architectural patterns do you rely on to separate business logic from presentation layers in a full-stack application?

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Crum & Forster candidates report.

Day 1 — Map the Crum & Forster loop

Write out the reported sequence: Recruiter Phone Screen, Hiring Manager Interview, Technical Evaluations, Onsite Interview Day.
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 C# (.NET)

Spend the session on C# (.NET), which Crum & Forster candidates report being tested on.
Write one worked example in C# (.NET) and time yourself on it.

Deliverable: One timed worked example in C# (.NET).

Day 3 — Work iSeries / AS400 Development

Spend the session on iSeries / AS400 Development, which Crum & Forster candidates report being tested on.
Write one worked example in iSeries / AS400 Development and time yourself on it.

Deliverable: One timed worked example in iSeries / AS400 Development.

Day 4 — Work GraphQL

Spend the session on GraphQL, which Crum & Forster candidates report being tested on.
Write one worked example in GraphQL and time yourself on it.

Deliverable: One timed worked example in GraphQL.

Day 5 — Answer out loud: Technical & Architecture Deep-Dive

Answer aloud, timed: Explain how you would optimize a slow-performing database query in a C#.NET Core application.
Answer aloud, timed: What are the primary advantages of using GraphQL over traditional REST APIs in a high-traffic web application?

Deliverable: Spoken answers to 2 reported Technical & Architecture Deep-Dive question(s), under time.

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

Answer aloud, timed: Given a specific take-home technical topic, how would you research, structure, and present your findings to a panel of senior engineers?
Answer aloud, timed: Walk us through a live coding or system design scenario where you have to resolve a data synchronization conflict between two microservices.

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

Day 7 — Answer out loud: Behavioral & Collaboration

Answer aloud, timed: Describe a time when you had to work closely with a Business Analyst to clarify vague or conflicting system requirements.
Answer aloud, timed: How do you present complex technical trade-offs to non-technical business leaders, such as a divisional Vice President?

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

Behavioural preparation

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

Describe a time when you had to work closely with a Business Analyst to clarify vague or conflicting system re

Medium · Behavioral & Collaboration

Describe a time when you had to work closely with a Business Analyst to clarify vague or conflicting system requirements.

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?

How do you present complex technical trade-offs to non-technical business leaders, such as a divisional Vice P

Medium · Behavioral & Collaboration

How do you present complex technical trade-offs to non-technical business leaders, such as a divisional Vice President?

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?

Tell me about a time when you disagreed with a senior architect's technical direction. How did you handle the

Medium · Behavioral & Collaboration

Tell me about a time when you disagreed with a senior architect's technical direction. How did you handle the situation and reach a resolution?

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?

How do you plan to leverage your academic accomplishments and technical skill set to drive value in your futur

Medium · Behavioral & Collaboration

How do you plan to leverage your academic accomplishments and technical skill set to drive value in your future team?

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?