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Berkshire Hathaway Homestate Companies Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Berkshire Hathaway Homestate Companies, you are stepping into a crucial role that supports the technological backbone of a premier commercial insurance provider. Your work directly impacts how the business processes claims, manages policies, and assesses risk across a vast portfolio. Because the insurance industry relies heavily on data accuracy, regulatory compliance, and system reliability, your contributions are vital to maintaining the operational excellence the company is known for. This position requires a deep appreciation for enterprise-scale architecture and robust software design. You will be building, maintaining, and optimizing applications that internal teams use daily to make high-stakes financial and underwriting decisions.

This guide is scoped to a Software Engineer candidate at Berkshire Hathaway Homestate Companies.

Focus areas

  • SQL
  • Software Engineering Fundamentals
  • Interview Aptitude Testing

The Berkshire Hathaway Homestate Companies interview process

Berkshire Hathaway Homestate Companies candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Cognitive Assessment

Mandatory online aptitude and intelligence assessment that candidates must pass to proceed.

What to demonstrate

  • Mandatory online aptitude and intelligence assessment that candidates must pass to proceed
  • Depth in SQL

How to prepare

  • Answer aloud and timed: What is the next number in the sequence: 2, 6, 12, 20, 30, __?
  • Answer aloud and timed: If all Bloops are Razzies, and some Razzies are Lazzies, are all Bloops Lazzies?

Phone Screen

Discussion with Human Resources about background, availability, and alignment with corporate culture.

What to demonstrate

  • Discussion with Human Resources about background, availability, and alignment with corporate culture
  • Depth in SQL

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.

Onsite Interview Panel

Comprehensive interview with senior technical leads, development team, and occasionally executive leadership focusing on deep theoretical knowledge.

What to demonstrate

  • Comprehensive interview with senior technical leads, development team, and occasionally executive leadership focusing on deep theoretical knowledge
  • Depth in SQL

How to prepare

  • Answer aloud and timed: What is the exact difference between a mutex and a semaphore?
  • Answer aloud and timed: Explain the concept of dependency injection and the specific problem of tight coupling it solves.

Where candidates lose points

Master the Vocabulary

Interviewers here care deeply about technical terminology. Do not rely on "I know how to code it, I just don't know the name." Brush up on the exact definitions of design patterns, OOP principles, and database architectures.

Practice Timed Logic Tests

The initial aptitude screens are a hard barrier to entry. Spend time on platforms that offer timed IQ, spatial reasoning, and mathematical logic tests to get comfortable with the format and time pressure.

Maintain Professional Formality

Treat every interaction, from the HR screen to the technical panel, with high professional courtesy. This is a traditional corporate environment; formal communication and business-professional attire (even virtually) will serve you well.

Clarify the Reporting Structure

During your opportunity to ask questions, politely ask about the team's hierarchy and reporting lines. This shows that you value structure and helps you understand exactly where this Software Engineer role sits within the organization.

Questions & practice

Define polymorphism and provide a precise explanation of how it is implemented at compile-time versus runtime.

Medium · Theoretical Computer Science and Termino

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 exact difference between a mutex and a semaphore?

Medium · Theoretical Computer Science and Termino

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 do you resolve a circular reference in a relational database schema?

Medium · SQL and Database Management

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?

Write a query using a Common Table Expression (CTE) to find employees who earn more than their direct managers

Medium · SQL and Database Management

Write a query using a Common Table Expression (CTE) to find employees who earn more than their direct managers.

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

Explain the precise steps a database engine takes when executing a query with a GROUP BY and HAVING clause.

Medium · SQL and Database Management

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

What is a covering index, and how does it improve query execution time?

Medium · SQL and Database Management

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

What is the next number in the sequence: 2, 6, 12, 20, 30, __?

Medium · Aptitude and Logical Reasoning

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

If all Bloops are Razzies, and some Razzies are Lazzies, are all Bloops Lazzies?

Medium · Aptitude and Logical Reasoning

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

You have 8 identical-looking balls, but one is slightly heavier. Using a balance scale, how can you find the h

Medium · Aptitude and Logical Reasoning

You have 8 identical-looking balls, but one is slightly heavier. Using a balance scale, how can you find the heavy ball in exactly two weighings?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Explain the concept of dependency injection and the specific problem of tight coupling it solves.

Medium · Theoretical Computer Science and Termino

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 the CAP theorem and explain why a system cannot simultaneously guarantee all three properties.

Medium · Theoretical Computer Science and Termino

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

What are the differences between optimistic and pessimistic locking in a database context?

Medium · Theoretical Computer Science and Termino

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

  1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
  2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
  3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
  4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

  • Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
  • How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Berkshire Hathaway Homestate Companies candidates report.

Day 1 — Map the Berkshire Hathaway Homestate Companies loop

  • Write out the reported sequence: Cognitive Assessment, Phone Screen, Onsite Interview Panel.
  • 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 3 reported rounds, with the weakest marked.

Day 2 — Work SQL

  • Spend the session on SQL, which Berkshire Hathaway Homestate Companies candidates report being tested on.
  • Write one worked example in SQL and time yourself on it.

Deliverable: One timed worked example in SQL.

Day 3 — Work Software Engineering Fundamentals

  • Spend the session on Software Engineering Fundamentals, which Berkshire Hathaway Homestate Companies candidates report being tested on.
  • Write one worked example in Software Engineering Fundamentals and time yourself on it.

Deliverable: One timed worked example in Software Engineering Fundamentals.

Day 4 — Work Interview Aptitude Testing

  • Spend the session on Interview Aptitude Testing, which Berkshire Hathaway Homestate Companies candidates report being tested on.
  • Write one worked example in Interview Aptitude Testing and time yourself on it.

Deliverable: One timed worked example in Interview Aptitude Testing.

Day 5 — Answer out loud: Aptitude and Logical Reasoning

  • Answer aloud, timed: What is the next number in the sequence: 2, 6, 12, 20, 30, __?
  • Answer aloud, timed: If all Bloops are Razzies, and some Razzies are Lazzies, are all Bloops Lazzies?

Deliverable: Spoken answers to 2 reported Aptitude and Logical Reasoning question(s), under time.

Day 6 — Answer out loud: Theoretical Computer Science and Terminology

  • Answer aloud, timed: Define polymorphism and provide a precise explanation of how it is implemented at compile-time versus runtime.
  • Answer aloud, timed: What is the exact difference between a mutex and a semaphore?

Deliverable: Spoken answers to 2 reported Theoretical Computer Science and Terminology question(s), under time.

Day 7 — Answer out loud: SQL and Database Management

  • Answer aloud, timed: Write a query using a Common Table Expression (CTE) to find employees who earn more than their direct managers.
  • Answer aloud, timed: Explain the precise steps a database engine takes when executing a query with a GROUP BY and HAVING clause.

Deliverable: Spoken answers to 2 reported SQL and Database Management question(s), under time.

Behavioural preparation

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

Unblock an engineer without taking the keyboard

Easy · mentoring · leases · at-least-once

A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.

Approach

  1. Ask before diagnosing, and ask for things answerable from data they already have: the attempt count on the job rows that produced duplicates, the handler's observed duration against its lease expiry, and whether the duplicate rows share a natural key that a unique constraint could have caught.
  2. Teach the shape rather than the answer. A lease cannot distinguish a dead worker from a slow one, so a handler that outruns its lease is running twice by design, and deploys deliver the other half by killing handlers mid-run on every rollout. Both of their candidate theories produce identical duplicate rows, which is why the evidence has to come from timings rather than from argument.
  3. Hand over a checklist they execute: a natural key on every write the handler performs so the second copy collides rather than appends, the record of intent written before any external effect, a lease heartbeat while running, and the metric that shows it working.
  4. Keep ownership with them deliberately. Pair on the first write, then step back; if you finish it yourself you have closed one ticket and left the same person stuck on the next redelivery.

Follow-up

  • How would you distinguish a genuine double-delivery from a lease expiry using only the data already stored?
  • Their handler calls an external endpoint before recording that it did. What do you tell them to change first?

Estimate work you have never done and defend the range

Hard · estimation · backfills · expand-contract

You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.

Approach

  1. Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
  2. Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
  3. Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
  4. Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.

Follow-up

  • How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
  • Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?

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?