Hub International Software Engineer Interview Guide

The 60-second brief

A Software Engineer at Hub International plays a pivotal role in driving the digital transformation of one of the world's leading insurance brokerages. In this role, you are not just writing code; you are building and maintaining the critical digital infrastructure that enables seamless transactions, complex risk management, and data-driven decision-making. Whether you are focused on core application development or stepping into a specialized track like the Information Management Engineer, your work directly impacts how millions of clients manage their risk and secure their futures. At Hub International, engineering is highly collaborative and integrated deeply with the business. You will work on modernizing legacy systems, developing cloud-native solutions, and building robust data pipelines that process massive volumes of financial and client data.

This guide is scoped to a Software Engineer candidate at Hub International.

Focus areas

• Information Management
• Technical Interviewing
• Coding Assessment (Algorithmic)

The Hub International interview process

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

Initial Contact

The process begins with your initial contact, where you will be introduced to the opportunity.

What to demonstrate

• The process begins with your initial contact, where you will be introduced to the opportunity
• Depth in Information Management

How to prepare

• Answer aloud and timed: Write pseudo-code to solve a basic data manipulation problem (such as filtering or formatting a dataset) using a simple text editor.
• Answer aloud and timed: Explain the difference between structured and unstructured data, and how you would design a schema to accommodate both.

Behavioral Alignment

Focus on high-level behavioral alignment to gauge fit with the team and culture.

What to demonstrate

• Focus on high-level behavioral alignment to gauge fit with the team and culture
• Depth in Information Management

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

Technical Preparation

Prepare for technical and coding assessments that will follow in later stages.

What to demonstrate

• Prepare for technical and coding assessments that will follow in later stages
• Depth in Information Management

How to prepare

• Answer aloud and timed: Tell me about a time you had to deal with a difficult technical challenge on your team. How did you resolve it?
• Answer aloud and timed: Describe a situation where you had to explain a complex technical concept to a non-technical business stakeholder.

Final Offer Stage

The process concludes with discussions around the final offer and job details.

What to demonstrate

• The process concludes with discussions around the final offer and job details
• Depth in Information Management

How to prepare

• Answer aloud and timed: How do you handle a situation where project requirements change mid-sprint?
• Answer aloud and timed: Share an example of a time you proactively identified a bottleneck in a software delivery process and took steps to improve it.

Where candidates lose points

Embrace the Conversation

Treat the interview as a collaborative, two-way dialogue. Ask thoughtful questions about the team's daily challenges, their technical stack, and how they celebrate success.

Focus on the "Why

When writing pseudo-code, focus on explaining why you are choosing a specific logical path. The interviewers care much more about your structured thinking and problem-solving approach than perfect syntax.

Be Honest About Challenges

The hiring team is very candid about the difficulties and legacy systems they work with. Show maturity by discussing how you approach technical debt and maintain motivation during challenging projects.

Highlight Your Business Acumen

Because Hub International is a major financial services organization, showing that you understand how your code impacts the bottom line and client experience will set you apart from other candidates.

Questions & practice

Write pseudo-code to solve a basic data manipulation problem (such as filtering or formatting a dataset) using

Medium · Technical & Problem Solving

Write pseudo-code to solve a basic data manipulation problem (such as filtering or formatting a dataset) using a simple text editor.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

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

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

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

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

How do you ensure data integrity and optimize query performance when working with large-scale databases?

Medium · Technical & Problem Solving

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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

• The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
• Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Explain the difference between structured and unstructured data, and how you would design a schema to accommod

Medium · Technical & Problem Solving

Explain the difference between structured and unstructured data, and how you would design a schema to accommodate both.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Design the bulk write endpoint a migration script retries blindly

Medium · batch api · partial failure · idempotency

A customer's migration script pushes 2 million resources through POST /v1/resources:batch, up to 500 items per call, and retries any call that errors or times out. Within a call some items fail validation, some collide with rows that already exist, and some succeed. Specify the request and response shape, whether a batch is atomic or per-item, how idempotency works for the call and for each item, the status code for a mixed outcome, the size and item-count limits with their error codes, and exactly what the script does after a timeout mid-batch.

Approach

1. Choose atomicity deliberately and price it. All-or-nothing means one transaction holding locks for the whole batch on a primary already absorbing about 1.2k writes/second, which bounds batch size by lock duration, and it turns one bad row into 499 rejections the script must resend. Per-item partial success is the right default for a migration, and the contract's job is then to make a partial outcome impossible to miss.
2. Require a client-supplied id on every item and echo it in every result. Deriving a per-item key from the array index breaks the first time the script resends a batch with the failures removed: the indices shift, previously-succeeded items acquire new keys, and they are created a second time.
3. Key the effects at two levels. The call's Idempotency-Key covers an exact resend of the same bytes; per-item keys of (tenant_id, client_item_id) make a partially-applied batch safe to resend whole. Resending an identical batch must reproduce the same per-item results, not 500 conflicts the script has to interpret.
4. Answer a mixed outcome with one status plus per-item detail: 200, or 207 borrowed from WebDAV if you prefer it - document whichever you pick - carrying an array of client_item_id, per-item status, and either resource_id or an error code from the same taxonomy the single-item endpoint uses. Reserve 4xx for the request as a whole: unparseable body, too many items, payload over the limit (413). Put a failed count at the top level so that even a script checking only the cheapest thing cannot conclude success while rows were dropped.

Follow-up

• At 500 items per call, what is the wall-clock time for 2 million rows, and what pacing do you publish so the migration does not become an incident?
• One item in every batch fails with the same code. How does the script discover that without a human reading logs?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

1. Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
2. Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
3. Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
4. Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

• The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
• You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics Hub International candidates report.

Day 1 — Map the Hub International loop

• Write out the reported sequence: Initial Contact, Behavioral Alignment, Technical Preparation, Final Offer Stage.
• 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 Information Management

• Spend the session on Information Management, which Hub International candidates report being tested on.
• Write one worked example in Information Management and time yourself on it.

Deliverable: One timed worked example in Information Management.

Day 3 — Work Technical Interviewing

• Spend the session on Technical Interviewing, which Hub International candidates report being tested on.
• Write one worked example in Technical Interviewing and time yourself on it.

Deliverable: One timed worked example in Technical Interviewing.

Day 4 — Work Coding Assessment (Algorithmic)

• Spend the session on Coding Assessment (Algorithmic), which Hub International candidates report being tested on.
• Write one worked example in Coding Assessment (Algorithmic) and time yourself on it.

Deliverable: One timed worked example in Coding Assessment (Algorithmic).

Day 5 — Answer out loud: Technical & Problem Solving

• Answer aloud, timed: Write pseudo-code to solve a basic data manipulation problem (such as filtering or formatting a dataset) using a simple text editor.
• Answer aloud, timed: Explain the difference between structured and unstructured data, and how you would design a schema to accommodate both.

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

Day 6 — Answer out loud: Behavioral & Collaboration

• Answer aloud, timed: Tell me about a time you had to deal with a difficult technical challenge on your team. How did you resolve it?
• Answer aloud, timed: Describe a situation where you had to explain a complex technical concept to a non-technical business stakeholder.

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

Day 7 — Answer out loud: Culture Fit & Career Alignment

• Answer aloud, timed: What are your career goals, and how do you see yourself growing at Hub International?
• Answer aloud, timed: What attracts you to the insurance and financial services technology space?

Deliverable: Spoken answers to 2 reported Culture Fit & Career Alignment question(s), under time.

Behavioural preparation

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

Describe your experience with integration patterns and how you handle data exchange between disparate systems.

Medium · Technical & Problem Solving

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell me about a time you had to deal with a difficult technical challenge on your team. How did you resolve it

Medium · Behavioral & Collaboration

Tell me about a time you had to deal with a difficult technical challenge on your team. How did you resolve it?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a situation where you had to explain a complex technical concept to a non-technical business stakehol

Medium · Behavioral & Collaboration

Describe a situation where you had to explain a complex technical concept to a non-technical business stakeholder.

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you handle a situation where project requirements change mid-sprint?

Medium · Behavioral & Collaboration

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Share an example of a time you proactively identified a bottleneck in a software delivery process and took ste

Medium · Behavioral & Collaboration

Share an example of a time you proactively identified a bottleneck in a software delivery process and took steps to improve it.

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

What are your career goals, and how do you see yourself growing at Hub International?

Medium · Culture Fit & Career Alignment

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

What attracts you to the insurance and financial services technology space?

Medium · Culture Fit & Career Alignment

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you balance the need for rapid feature delivery with maintaining high code quality and technical standa

Medium · Culture Fit & Career Alignment

How do you balance the need for rapid feature delivery with maintaining high code quality and technical standards?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe your ideal team environment and the type of leadership style that helps you perform at your best.

Medium · Culture Fit & Career Alignment

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?