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Travelers Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Travelers, you play a vital role in building, modernizing, and maintaining the mission-critical systems that power one of North America's largest insurance operations. The engineering organization at Travelers spans multiple business units, including Personal Insurance, Commercial Insurance, Bond & Specialty Insurance, and Legal & Compliance. Engineers in these groups develop scalable RESTful APIs, cloud-native enterprise services, enterprise AI tooling, and customer-facing web applications that process millions of transactions daily. The work you perform directly impacts how underwriting risk is calculated, how policyholders manage coverage, and how internal business teams leverage machine learning and cloud automation.

This guide is scoped to a Software Engineer candidate at Travelers.

Focus areas

  • AWS
  • Project Deep Dive
  • REST APIs

The Travelers interview process

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

Phone Screen

Brief introductory call with an internal recruiter to discuss the candidate's background and role fit.

What to demonstrate

  • Brief introductory call with an internal recruiter to discuss the candidate's background and role fit
  • Depth in AWS

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 Panel Interview

One or two interviews focusing on technical skills and engineering experience, potentially including a light online assessment.

What to demonstrate

  • One or two interviews focusing on technical skills and engineering experience, potentially including a light online assessment
  • Depth in AWS

How to prepare

  • Answer aloud and timed: Describe a situation where a technical design decision you made turned out to be flawed. How did you identify the issue and remediate it?
  • Answer aloud and timed: How do you design APIs to ensure backward compatibility and smooth integration with legacy enterprise systems?

Behavioral Panel Interview

Interviews assessing cultural fit and communication style through behavioral questions.

What to demonstrate

  • Interviews assessing cultural fit and communication style through behavioral questions
  • Depth in AWS

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

Where candidates lose points

Frame your answers using the STAR method (Situation, Task, Action, Result) for behavioral questions

Focus heavily on your specific contributions and technical decisions.

Be Ready to Unpack Your Resume

Every technology, framework, or project listed on your resume is fair game. Be prepared to explain the architectural choices, challenges faced, and trade-offs made in any past role you list.

Emphasize Testing and Maintainability

Highlight your dedication to writing testable code, participating in peer code reviews, and using modern CI/CD automation. Travelers places high value on operational resilience.

Demonstrate Business Awareness

Show that you understand how software engineering supports business goals. Be ready to explain how your technical decisions positively impacted user experience, operational efficiency, or system stability.

Questions & practice

Describe how Merge Sort works, explain its time complexity, and trace an example array step-by-step.

Medium · Web Development and Core Engineering Fun

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?

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?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

  1. Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
  2. For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
  3. For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
  4. Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

  • A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
  • Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

How do you optimize complex SQL queries or database schemas to prevent performance bottlenecks in production a

Medium · Web Development and Core Engineering Fun

How do you optimize complex SQL queries or database schemas to prevent performance bottlenecks in production applications?

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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

  1. Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
  2. Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
  3. Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
  4. Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

  • The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
  • A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Walk through your most recent major software project, detailing the architecture and why specific frameworks o

Medium · Architecture and Technical Decision-Maki

Walk through your most recent major software project, detailing the architecture and why specific frameworks or technologies were chosen.

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?

How do you evaluate trade-offs when choosing between a monolithic architecture and microservices for an enterp

Medium · Architecture and Technical Decision-Maki

How do you evaluate trade-offs when choosing between a monolithic architecture and microservices for an enterprise application?

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?

Describe a situation where a technical design decision you made turned out to be flawed. How did you identify

Medium · Architecture and Technical Decision-Maki

Describe a situation where a technical design decision you made turned out to be flawed. How did you identify the issue and remediate it?

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?

How do you design APIs to ensure backward compatibility and smooth integration with legacy enterprise systems?

Medium · Architecture and Technical Decision-Maki

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?

What considerations do you take into account when scaling cloud services on AWS to handle high transaction vol

Medium · Architecture and Technical Decision-Maki

What considerations do you take into account when scaling cloud services on AWS to handle high transaction volumes?

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?

Explain the complete lifecycle of an HTTP request from the moment a user enters a URL to when the webpage rend

Medium · Web Development and Core Engineering Fun

Explain the complete lifecycle of an HTTP request from the moment a user enters a URL to when the webpage renders.

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 core constraints and principles of a RESTful API architecture, and how do they differ from RPC or

Medium · Web Development and Core Engineering Fun

What are the core constraints and principles of a RESTful API architecture, and how do they differ from RPC or GraphQL?

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. 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?

Explain how front-end applications built with Angular or React communicate securely with back-end services.

Medium · Web Development and Core Engineering Fun

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?

How do you structure unit tests and integration tests to ensure reliable test coverage without slowing down CI

Medium · Software Quality, Testing, and Developme

How do you structure unit tests and integration tests to ensure reliable test coverage without slowing down CI/CD pipelines?

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. 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?

What process do you follow when reviewing a pull request, and how do you deliver constructive feedback to team

Medium · Software Quality, Testing, and Developme

What process do you follow when reviewing a pull request, and how do you deliver constructive feedback to team members?

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 your approach to identifying, isolating, and fixing bugs in production cloud applications.

Medium · Software Quality, Testing, and Developme

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?

How do you incorporate security best practices, such as input validation and authorization checks, into your d

Medium · Software Quality, Testing, and Developme

How do you incorporate security best practices, such as input validation and authorization checks, into your development workflow?

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?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

  1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
  2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
  3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
  4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

  • The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
  • How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Travelers candidates report.

Day 1 — Map the Travelers loop

  • Write out the reported sequence: Phone Screen, Technical Panel Interview, Behavioral Panel 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 AWS

  • Spend the session on AWS, which Travelers candidates report being tested on.
  • Write one worked example in AWS and time yourself on it.

Deliverable: One timed worked example in AWS.

Day 3 — Work Project Deep Dive

  • Spend the session on Project Deep Dive, which Travelers candidates report being tested on.
  • Write one worked example in Project Deep Dive and time yourself on it.

Deliverable: One timed worked example in Project Deep Dive.

Day 4 — Work REST APIs

  • Spend the session on REST APIs, which Travelers candidates report being tested on.
  • Write one worked example in REST APIs and time yourself on it.

Deliverable: One timed worked example in REST APIs.

Day 5 — Answer out loud: Architecture and Technical Decision-Making

  • Answer aloud, timed: Walk through your most recent major software project, detailing the architecture and why specific frameworks or technologies were chosen.
  • Answer aloud, timed: How do you evaluate trade-offs when choosing between a monolithic architecture and microservices for an enterprise application?

Deliverable: Spoken answers to 2 reported Architecture and Technical Decision-Making question(s), under time.

Day 6 — Answer out loud: Web Development and Core Engineering Fundamentals

  • Answer aloud, timed: Explain the complete lifecycle of an HTTP request from the moment a user enters a URL to when the webpage renders.
  • Answer aloud, timed: What are the core constraints and principles of a RESTful API architecture, and how do they differ from RPC or GraphQL?

Deliverable: Spoken answers to 2 reported Web Development and Core Engineering Fundamentals question(s), under time.

Day 7 — Answer out loud: Software Quality, Testing, and Development Process

  • Answer aloud, timed: How do you structure unit tests and integration tests to ensure reliable test coverage without slowing down CI/CD pipelines?
  • Answer aloud, timed: What process do you follow when reviewing a pull request, and how do you deliver constructive feedback to team members?

Deliverable: Spoken answers to 2 reported Software Quality, Testing, and Development Process question(s), under time.

Behavioural preparation

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

Describe your experience with automated deployment, version control with Git, and continuous integration workf

Medium · Software Quality, Testing, and Developme

Describe your experience with automated deployment, version control with Git, and continuous integration workflows.

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 time when you disagreed with a senior engineer or project manager on a technical approach. How did

Medium · Behavioral and Cross-Functional Collabor

Describe a time when you disagreed with a senior engineer or project manager on a technical approach. How did you resolve the situation?

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 translate complex technical concepts or trade-offs into language that business stakeholders and Bus

Medium · Behavioral and Cross-Functional Collabor

How do you translate complex technical concepts or trade-offs into language that business stakeholders and Business Analysts can easily understand?

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 when you were working on a project with ambiguous or changing requirements. How did you m

Medium · Behavioral and Cross-Functional Collabor

Tell me about a time when you were working on a project with ambiguous or changing requirements. How did you maintain progress?

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?

Give an example of how you handle competing technical priorities and tight production deadlines.

Medium · Behavioral and Cross-Functional Collabor

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 project where you had to collaborate closely with cross-functional teams, such as Quality Assurance

Medium · Behavioral and Cross-Functional Collabor

Describe a project where you had to collaborate closely with cross-functional teams, such as Quality Assurance or DevOps.

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?