Interview guides · National Indemnity

National Indemnity Software Engineer Interview Guide

National Indemnity Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at National Indemnity plays a pivotal role in maintaining and modernizing the core financial and operational infrastructure of one of the nation's leading insurance providers. As a key subsidiary of Berkshire Hathaway, the company relies heavily on robust, scalable, and highly secure software systems to process high-volume transactions, manage complex risk portfolios, and streamline claims processing. Engineers here do not just write code; they build and optimize the digital backbone that supports multi-million dollar underwriting decisions and claims workflows daily. In this role, you will work closely with proprietary and third-party enterprise platforms, such as Facets (the company's core claims processing system), to ensure seamless integration and maximum operational efficiency.

This guide is scoped to a Software Engineer candidate at National Indemnity.

Focus areas

Programming Ability (General)
Claims Processing Systems
Programming Interview Screening (Coding Focus)

The National Indemnity interview process

National Indemnity candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Online Application

Submit your resume through the application portal, which allows for easy import and pre-filling of fields.

What to demonstrate

Submit your resume through the application portal, which allows for easy import and pre-filling of fields
Depth in Programming Ability (General)

How to prepare

Answer aloud and timed: Explain the difference between an inner join, a left join, and a full outer join, and describe a scenario where you would use each.
Answer aloud and timed: How do you optimize a slow-running SQL query, and what role do indexes play in database performance?

Resume Review

Your application will be reviewed, and you may be invited to the primary interview based on your qualifications.

What to demonstrate

Your application will be reviewed, and you may be invited to the primary interview based on your qualifications
Depth in Programming Ability (General)

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.

Primary Interview

Participate in an interview focusing on behavioral questions, SQL familiarity, and cultural fit, conducted onsite or virtually.

What to demonstrate

Participate in an interview focusing on behavioral questions, SQL familiarity, and cultural fit, conducted onsite or virtually
Depth in Programming Ability (General)

How to prepare

Answer aloud and timed: Have you ever worked with large relational database management systems, and how do you handle data integrity constraints?
Answer aloud and timed: What is your experience with claims processing systems, and are you familiar with platforms like Facets?

Where candidates lose points

Focus on SQL

Do not underestimate the database portion of the interview. Be ready to write queries on the spot and discuss database design and optimization concepts in detail.

Keep your answers analytical

When answering behavioral or situational questions, structure your responses logically. Focus on data, metrics, and clear decision-making processes rather than philosophical or abstract explanations.

Be proactive with communication

Since some candidates have reported slower response times from recruiting, keep a detailed record of your communications and follow up politely but firmly if you do not receive a timely update.

Questions & practice

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?

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?

Explain the difference between an inner join, a left join, and a full outer join, and describe a scenario wher

Medium · Database & SQL Fundamentals

Explain the difference between an inner join, a left join, and a full outer join, and describe a scenario where you would use each.

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?

How do you optimize a slow-running SQL query, and what role do indexes play in database performance?

Medium · Database & SQL Fundamentals

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

Given a schema with claims and policyholder tables, write a query to find the total claims paid per state over

Medium · Database & SQL Fundamentals

Given a schema with claims and policyholder tables, write a query to find the total claims paid per state over the last fiscal year.

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?

What is database normalization, and when might you intentionally denormalize a database schema?

Medium · Database & SQL Fundamentals

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

How would you design an API integration to sync third-party claim data with an internal legacy database?

Medium · Domain & Systems Integration

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 ensure data security and compliance when handling sensitive customer or financial information?

Medium · Domain & Systems Integration

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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics National Indemnity candidates report.

Day 1 — Map the National Indemnity loop

Write out the reported sequence: Online Application, Resume Review, Primary Interview.
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 Programming Ability (General)

Spend the session on Programming Ability (General), which National Indemnity candidates report being tested on.
Write one worked example in Programming Ability (General) and time yourself on it.

Deliverable: One timed worked example in Programming Ability (General).

Day 3 — Work Claims Processing Systems

Spend the session on Claims Processing Systems, which National Indemnity candidates report being tested on.
Write one worked example in Claims Processing Systems and time yourself on it.

Deliverable: One timed worked example in Claims Processing Systems.

Day 4 — Work Programming Interview Screening (Coding Focus)

Spend the session on Programming Interview Screening (Coding Focus), which National Indemnity candidates report being tested on.
Write one worked example in Programming Interview Screening (Coding Focus) and time yourself on it.

Deliverable: One timed worked example in Programming Interview Screening (Coding Focus).

Day 5 — Answer out loud: Database & SQL Fundamentals

Answer aloud, timed: Explain the difference between an inner join, a left join, and a full outer join, and describe a scenario where you would use each.
Answer aloud, timed: How do you optimize a slow-running SQL query, and what role do indexes play in database performance?

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

Day 6 — Answer out loud: Domain & Systems Integration

Answer aloud, timed: What is your experience with claims processing systems, and are you familiar with platforms like Facets?
Answer aloud, timed: How would you design an API integration to sync third-party claim data with an internal legacy database?

Deliverable: Spoken answers to 2 reported Domain & Systems Integration question(s), under time.

Day 7 — Answer out loud: Behavioral & Analytical Reasoning

Answer aloud, timed: Tell me about a time when you had to debug a critical production issue under tight time constraints. What was your analytical approach?
Answer aloud, timed: Describe a situation where you had a disagreement with a team member regarding a technical implementation. How did you resolve it?

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

Behavioural preparation

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

Have you ever worked with large relational database management systems, and how do you handle data integrity c

Medium · Database & SQL Fundamentals

Have you ever worked with large relational database management systems, and how do you handle data integrity constraints?

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?

What is your experience with claims processing systems, and are you familiar with platforms like Facets?

Medium · Domain & Systems Integration

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 had to work with a complex, poorly documented system. How did you navigate the integr

Medium · Domain & Systems Integration

Describe a time when you had to work with a complex, poorly documented system. How did you navigate the integration?

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 had to debug a critical production issue under tight time constraints. What was

Medium · Behavioral & Analytical Reasoning

Tell me about a time when you had to debug a critical production issue under tight time constraints. What was your analytical approach?

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 situation where you had a disagreement with a team member regarding a technical implementation. How

Medium · Behavioral & Analytical Reasoning

Describe a situation where you had a disagreement with a team member regarding a technical implementation. How did you resolve it?

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 working in the insurance industry, and how do you see your technical skills contribu

Medium · Behavioral & Analytical Reasoning

Why are you interested in working in the insurance industry, and how do you see your technical skills contributing to our business goals?

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 challenging programming project you completed. What were the main obstacles, and how did you o

Medium · Behavioral & Analytical Reasoning

Tell me about a challenging programming project you completed. What were the main obstacles, and how did you overcome them?

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?