Breeze Airways Software Engineer Interview Guide

The 60-second brief

A Software Engineer at Breeze Airways plays a pivotal role in building and scaling the digital backbone of the "world's nicest airline." In an industry historically burdened by legacy systems, Breeze Airways positions itself as a modern, technology-first carrier. As a engineer here, your work directly influences the guest experience—from booking and check-in to real-time flight updates—as well as the complex operations that keep aircraft flying safely and efficiently. The engineering ecosystem at Breeze Airways is highly diverse, spanning consumer-facing web applications, internal operational dashboards, and specialized aerospace domains. Depending on your team alignment, you may work on modern web architectures or dive into safety-critical systems such as avionics, electrical integration, or powerplant engineering.

This guide is scoped to a Software Engineer candidate at Breeze Airways.

Focus areas

• Programming Problem Solving
• Coding Challenges / Technical Tests
• Web Application Development

The Breeze Airways interview process

Breeze Airways candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Technical Screening

Initial assessment through a take-home coding assignment or automated coding challenge to verify programming skills.

What to demonstrate

• Initial assessment through a take-home coding assignment or automated coding challenge to verify programming skills
• Depth in Programming Problem Solving

How to prepare

• Answer aloud and timed: Build a functional web application that consumes a public API and displays filtered data in real time (Take-home assignment).
• Answer aloud and timed: Implement a algorithm to resolve scheduling conflicts for flight crews or aircraft routing.

Live Interviews

Interviews with engineering leaders and team members focusing on technical deep-dives, architectural discussions, and behavioral evaluations.

What to demonstrate

• Interviews with engineering leaders and team members focusing on technical deep-dives, architectural discussions, and behavioral evaluations
• Depth in Programming Problem Solving

How to prepare

• Answer aloud and timed: Write a program to parse and validate complex data payloads, ensuring robust error handling for corrupt inputs.
• Answer aloud and timed: Explain the trade-offs between different data structures when optimizing search latency for flight availability.

Where candidates lose points

Over-communicate during technical assessments

Whether you are walking through a live coding challenge or submitting a take-home assignment, clearly explain your thought process, the trade-offs you are making, and why you chose a particular architecture.

Refine your failure story

Do not shy away from discussing past mistakes. Prepare a structured narrative using the STAR method (Situation, Task, Action, Result) that highlights a real technical failure, what you learned, and how it made you a better engineer.

When discussing past projects, emphasize your personal contributions

Use "I" instead of "we" to clearly articulate the specific technical decisions you made and the direct impact of your work.

Questions & practice

Write a program to parse and validate complex data payloads, ensuring robust error handling for corrupt inputs

Medium · Coding & Technical Problem Solving

Write a program to parse and validate complex data payloads, ensuring robust error handling for corrupt inputs.

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?

Explain the trade-offs between different data structures when optimizing search latency for flight availabilit

Medium · Coding & Technical Problem Solving

Explain the trade-offs between different data structures when optimizing search latency for flight availability.

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?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

• 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
• A dashboard runs this for 40 owners in one page load. What changes about the design?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

• A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
• How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Build a functional web application that consumes a public API and displays filtered data in real time (Take-ho

Medium · Coding & Technical Problem Solving

Build a functional web application that consumes a public API and displays filtered data in real time (Take-home assignment).

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?

How do you design software interfaces to communicate reliably with physical hardware systems under strict late

Medium · Systems & Domain Knowledge

How do you design software interfaces to communicate reliably with physical hardware systems under strict latency constraints?

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 approach writing software that must comply with strict regulatory frameworks or safety-critical sta

Medium · Systems & Domain Knowledge

How do you approach writing software that must comply with strict regulatory frameworks or safety-critical standards?

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?

Detail a scenario where you had to integrate a modern software application with a legacy hardware system.

Medium · Systems & Domain Knowledge

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

• The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
• How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Breeze Airways candidates report.

Day 1 — Map the Breeze Airways loop

• Write out the reported sequence: Technical Screening, Live Interviews.
• 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 2 reported rounds, with the weakest marked.

Day 2 — Work Programming Problem Solving

• Spend the session on Programming Problem Solving, which Breeze Airways candidates report being tested on.
• Write one worked example in Programming Problem Solving and time yourself on it.

Deliverable: One timed worked example in Programming Problem Solving.

Day 3 — Work Coding Challenges / Technical Tests

• Spend the session on Coding Challenges / Technical Tests, which Breeze Airways candidates report being tested on.
• Write one worked example in Coding Challenges / Technical Tests and time yourself on it.

Deliverable: One timed worked example in Coding Challenges / Technical Tests.

Day 4 — Work Web Application Development

• Spend the session on Web Application Development, which Breeze Airways candidates report being tested on.
• Write one worked example in Web Application Development and time yourself on it.

Deliverable: One timed worked example in Web Application Development.

Day 5 — Answer out loud: Coding & Technical Problem Solving

• Answer aloud, timed: Build a functional web application that consumes a public API and displays filtered data in real time (Take-home assignment).
• Answer aloud, timed: Implement a algorithm to resolve scheduling conflicts for flight crews or aircraft routing.

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

Day 6 — Answer out loud: Systems & Domain Knowledge

• Answer aloud, timed: How do you design software interfaces to communicate reliably with physical hardware systems under strict latency constraints?
• Answer aloud, timed: Describe your experience with diagnostic tools and software used to monitor aircraft engine performance or electrical systems.

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

Day 7 — Answer out loud: Behavioral & Cultural Fit

• Answer aloud, timed: Who are you, and what have you built in your career that you are most proud of?
• Answer aloud, timed: Tell me about a significant technical failure you experienced. What caused it, how did you resolve it, and what did you learn?

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

Behavioural preparation

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

Implement a algorithm to resolve scheduling conflicts for flight crews or aircraft routing.

Medium · Coding & 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?

Describe your experience with diagnostic tools and software used to monitor aircraft engine performance or ele

Medium · Systems & Domain Knowledge

Describe your experience with diagnostic tools and software used to monitor aircraft engine performance or electrical systems.

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?

Who are you, and what have you built in your career that you are most proud of?

Medium · Behavioral & Cultural Fit

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 significant technical failure you experienced. What caused it, how did you resolve it, and wha

Medium · Behavioral & Cultural Fit

Tell me about a significant technical failure you experienced. What caused it, how did you resolve it, and what did you learn?

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 rapidly mid-development?

Medium · Behavioral & Cultural Fit

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 had to collaborate with a non-technical stakeholder to solve a complex operational pr

Medium · Behavioral & Cultural Fit

Describe a time when you had to collaborate with a non-technical stakeholder to solve a complex operational problem.

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?