Interview guides · Berkshire Hathaway Specialty Insurance

Berkshire Hathaway Specialty Insurance Software Engineer Interview Guide

Berkshire Hathaway Specialty Insurance Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Berkshire Hathaway Specialty Insurance (BHSI), you play a vital role in building and scaling the digital platforms that drive commercial property, casualty, and specialty insurance products. You contribute directly to modern, full-stack applications and cloud-based ecosystems that support critical business operations, such as SME insurance and travel insurance domains. Your work directly impacts how brokers, customers, and internal teams interact with complex insurance workflows, ensuring seamless speed-to-market and high operational reliability. This role requires a balance of technical execution and collaborative problem-solving within a purposely non-bureaucratic, values-driven organization.

This guide is scoped to a Software Engineer candidate at Berkshire Hathaway Specialty Insurance.

Focus areas

RESTful APIs
Full-stack development
Unit testing

The Berkshire Hathaway Specialty Insurance interview process

Berkshire Hathaway Specialty Insurance 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 a preliminary assessment to evaluate your qualifications for the role.

What to demonstrate

The first step involves a preliminary assessment to evaluate your qualifications for the role
Depth in RESTful APIs

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 Assessments

You will undergo various technical evaluations to assess your coding skills and problem-solving abilities.

What to demonstrate

You will undergo various technical evaluations to assess your coding skills and problem-solving abilities
Depth in RESTful APIs

How to prepare

Answer aloud and timed: How have you approached full-stack feature delivery from concept through production in past environments?
Answer aloud and timed: What experience do you have designing and building RESTful APIs or service-oriented applications?

Behavioral Evaluations

Interviews focused on understanding your interpersonal skills and alignment with company values.

What to demonstrate

Interviews focused on understanding your interpersonal skills and alignment with company values
Depth in RESTful APIs

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 evaluations above and write down what you would ask to confirm before it.

Final Discussions

The concluding stage where final evaluations and discussions about the role take place.

What to demonstrate

The concluding stage where final evaluations and discussions about the role take place
Depth in RESTful APIs

How to prepare

Answer aloud and timed: 1–2 sentences introducing the category and what it tests. These questions test your ability to structure scalable solutions, reason about application layers, and make pragmatic trade-offs.
Answer aloud and timed: How would you design a scalable digital platform component to support distributed insurance operations?

Where candidates lose points

Emphasize Simplicity

In technical design and coding discussions, always favor clean, maintainable, and straightforward solutions over complex over-engineering.

Know Your Resume

Be prepared to walk through your past projects, technical stack choices, and specific contributions in detail during initial conversations.

Demonstrate Collaboration

Highlight how you partner effectively with QA, product owners, and engineering peers to drive projects from concept to production.

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?

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?

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?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

1–2 sentences introducing the category and what it tests. This section evaluates your practical hands-on exper

Medium · Technical and Resume-Based Discussions

1–2 sentences introducing the category and what it tests. This section evaluates your practical hands-on experience, familiarity with specific technology stacks, and your ability to articulate past technical contributions.

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?

Walk me through your work experience, projects, and the specific technologies you used in your previous roles.

Medium · Technical and Resume-Based Discussions

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 have you approached full-stack feature delivery from concept through production in past environments?

Medium · Technical and Resume-Based Discussions

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 experience do you have designing and building RESTful APIs or service-oriented applications?

Medium · Technical and Resume-Based Discussions

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?

1–2 sentences introducing the category and what it tests. These questions test your ability to structure scala

Medium · System Design and Architecture

1–2 sentences introducing the category and what it tests. These questions test your ability to structure scalable solutions, reason about application layers, and make pragmatic trade-offs.

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 digital platform component to support distributed insurance operations?

Medium · System Design and Architecture

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?

Discuss how you manage data storage and retrieval layers when building multi-tier applications.

Medium · System Design and Architecture

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 strategies do you use to ensure maintainability and cleanliness when expanding an existing codebase?

Medium · System Design and Architecture

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 troubleshooting performance issues and application incidents across distributed systems?

Medium · System Design and Architecture

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 Berkshire Hathaway Specialty Insurance candidates report.

Day 1 — Map the Berkshire Hathaway Specialty Insurance loop

Write out the reported sequence: Initial Screening, Technical Assessments, Behavioral Evaluations, Final 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 RESTful APIs

Spend the session on RESTful APIs, which Berkshire Hathaway Specialty Insurance candidates report being tested on.
Write one worked example in RESTful APIs and time yourself on it.

Deliverable: One timed worked example in RESTful APIs.

Day 3 — Work Full-stack development

Spend the session on Full-stack development, which Berkshire Hathaway Specialty Insurance candidates report being tested on.
Write one worked example in Full-stack development and time yourself on it.

Deliverable: One timed worked example in Full-stack development.

Day 4 — Work Unit testing

Spend the session on Unit testing, which Berkshire Hathaway Specialty Insurance candidates report being tested on.
Write one worked example in Unit testing and time yourself on it.

Deliverable: One timed worked example in Unit testing.

Day 5 — Answer out loud: Technical and Resume-Based Discussions

Answer aloud, timed: 1–2 sentences introducing the category and what it tests. This section evaluates your practical hands-on experience, familiarity with specific technology stacks, and your ability to articulate past technical contributions.
Answer aloud, timed: Walk me through your work experience, projects, and the specific technologies you used in your previous roles.

Deliverable: Spoken answers to 2 reported Technical and Resume-Based Discussions question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

Answer aloud, timed: 1–2 sentences introducing the category and what it tests. These questions test your ability to structure scalable solutions, reason about application layers, and make pragmatic trade-offs.
Answer aloud, timed: How would you design a scalable digital platform component to support distributed insurance operations?

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

Day 7 — Answer out loud: Behavioral and Cultural Alignment

Answer aloud, timed: 1–2 sentences introducing the category and what it tests. These inquiries measure how well you collaborate in cross-functional teams, navigate ambiguity, and align with core values like integrity and respect.
Answer aloud, timed: Why are you interested in joining BHSI, and how do your values align with our organizational culture?

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

Behavioural preparation

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

How do you handle writing and maintaining unit tests as part of your everyday development workflow?

Medium · Technical and Resume-Based Discussions

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?

Can you explain your experience working with cloud-based application concepts and distributed systems?

Medium · Technical and Resume-Based Discussions

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?

1–2 sentences introducing the category and what it tests. These inquiries measure how well you collaborate in

Medium · Behavioral and Cultural Alignment

1–2 sentences introducing the category and what it tests. These inquiries measure how well you collaborate in cross-functional teams, navigate ambiguity, and align with core values like integrity and respect.

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?

Why are you interested in joining BHSI, and how do your values align with our organizational culture?

Medium · Behavioral and Cultural Alignment

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 when you collaborated closely with product owners or quality assurance peers to resolve a deli

Medium · Behavioral and Cultural Alignment

Describe a time when you collaborated closely with product owners or quality assurance peers to resolve a delivery roadblock.

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 handle shifting priorities and backlog refinement in an Agile team environment?

Medium · Behavioral and Cultural Alignment

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 you contributed to continuous improvement or shared knowledge within your engineering tea

Medium · Behavioral and Cultural Alignment

Tell me about a time you contributed to continuous improvement or shared knowledge within your engineering 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?