As a Software Engineer at Braze, you build the core infrastructure and user-facing applications that power real-time customer engagement for leading global brands. Your work directly impacts how millions of end-users experience personalized notifications, in-app messaging surfaces like Content Cards, cross-channel campaigns, and real-time streaming data pipelines. Engineers at Braze operate at immense scale, designing systems capable of processing billions of high-throughput events and delivering time-sensitive communications with low latency and rock-solid reliability. The role demands a balance between robust backend architectural thinking and modern frontend development. Whether you join a division dedicated to channels infrastructure, cloud data ingestion, or customer-facing dashboard interfaces, you will work with modern technologies including TypeScript, React, Node.js, Python, and distributed PostgreSQL databases. You will be tasked with solving complex problems around asynchronous event orchestration, state management, real-time targeting, and high-availability message delivery engines. Joining Braze as a Software Engineer means taking ownership of mission-critical systems from architectural design to deployment and monitoring. You will collaborate closely with product managers, UX designers, and platform engineers to build features that are scalable, maintainable, and highly resilient.
Recruiter Call
reportedInitial conversation focusing on your background, project experience, and salary expectations.
What to demonstrate
- Initial conversation focusing on your background, project experience, and salary expectations
- Depth in Algorithms (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.
Technical Screen
reportedParticipate in a live technical screen or complete a proctored assessment featuring algorithmic problem-solving.
What to demonstrate
- Participate in a live technical screen or complete a proctored assessment featuring algorithmic problem-solving
- Depth in Algorithms (general)
How to prepare
- Answer aloud and timed: Given a stream of incoming user activity events, group and sort the event records based on dynamic pipeline rule definitions.
- Answer aloud and timed: Write an efficient algorithm to implement matching logic for a grid-based puzzle game, optimizing runtime and space complexity.
Virtual Onsite Loop
reportedComprehensive assessment including live code debugging, distributed system design, and a behavioral interview.
What to demonstrate
- Comprehensive assessment including live code debugging, distributed system design, and a behavioral interview
- Depth in Algorithms (general)
How to prepare
- Answer aloud and timed: Design an in-memory data structure that supports fast insertion, deletion, and threshold checking for dynamic time-series event metrics.
- Answer aloud and timed: Given a pre-packaged codebase containing failing unit tests for a text encryption algorithm, identify the logic errors and pass all tests without altering external interfaces.
Code Debugging Session
reportedCandidates debug and refactor existing application code with failing unit tests.
What to demonstrate
- Candidates debug and refactor existing application code with failing unit tests
- Depth in Algorithms (general)
How to prepare
- Answer aloud and timed: Review a legacy utility module with cryptic variable naming, trace the step-by-step execution path, and fix underlying edge-case bugs under strict time constraints.
- Answer aloud and timed: Analyze a broken state management routine in a web application framework and correct asynchronous race conditions causing intermittent test failures.
Behavioral Interview
reportedInterview with an engineering manager focusing on past experiences and cultural fit.
What to demonstrate
- Interview with an engineering manager focusing on past experiences and cultural fit
- Depth in Algorithms (general)
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 interview above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Master terminal-based debugging: In the debugging round, fast navigation and familiarity with running unit tests from the command line will save you valuable minutes.
Going into the loop without having done this.
Clarify vague prompts early: Interviewers at Braze sometimes present intentionally high-level prompts to test your communication and initiative. Ask clarifying questions to establish boundaries before coding.
Going into the loop without having done this.
Focus on data modeling in system design: Be prepared to write explicit SQL statements and draw out relational entity-relationship diagrams during system design sessions.
Going into the loop without having done this.
Maintain clear verbal communication during coding: Talk through your logic as you write code or debug issues. If you hit a roadblock, explain what you are checking and why; interviewers value an organized problem-solving approach.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a function to evaluate string pattern matching and handle complex nested parentheses validation with
Implement a function to evaluate string pattern matching and handle complex nested parentheses validation with depth constraints.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Solve a progressive matrix traversal problem, handling dynamic obstacles and scaling the solution across highe
Solve a progressive matrix traversal problem, handling dynamic obstacles and scaling the solution across higher dimensions.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Given a stream of incoming user activity events, group and sort the event records based on dynamic pipeline ru
Given a stream of incoming user activity events, group and sort the event records based on dynamic pipeline rule definitions.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Write an efficient algorithm to implement matching logic for a grid-based puzzle game, optimizing runtime and
Write an efficient algorithm to implement matching logic for a grid-based puzzle game, optimizing runtime and space complexity.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Given a pre-packaged codebase containing failing unit tests for a text encryption algorithm, identify the logi
Given a pre-packaged codebase containing failing unit tests for a text encryption algorithm, identify the logic errors and pass all tests without altering external interfaces.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Analyze a broken state management routine in a web application framework and correct asynchronous race conditi
Analyze a broken state management routine in a web application framework and correct asynchronous race conditions causing intermittent test failures.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
How do you approach working with ambiguous technical requirements when collaborating with cross-functional pro
How do you approach working with ambiguous technical requirements when collaborating with cross-functional product teams?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Design a relational database schema for a customer engagement campaign manager, including writing explicit SQL
Design a relational database schema for a customer engagement campaign manager, including writing explicit SQL queries for complex cohort targeting.
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?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Design an in-memory data structure that supports fast insertion, deletion, and threshold checking for dynamic
Design an in-memory data structure that supports fast insertion, deletion, and threshold checking for dynamic time-series event metrics.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 a high-throughput ad scraping and analytics pipeline capable of ingesting external web data and serving
Design a high-throughput ad scraping and analytics pipeline capable of ingesting external web data and serving real-time reporting dashboards.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Create an architecture for a real-time notification delivery engine that guarantees at-least-once message deli
Create an architecture for a real-time notification delivery engine that guarantees at-least-once message delivery across multiple mobile and web channels.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Outline a cloud data ingestion pipeline that transforms high-volume incoming webhook payloads into structured
Outline a cloud data ingestion pipeline that transforms high-volume incoming webhook payloads into structured database records.
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?
Describe a past project where you had to make a difficult architectural tradeoff between speed of delivery and
Describe a past project where you had to make a difficult architectural tradeoff between speed of delivery and long-term maintainability.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Review a legacy utility module with cryptic variable naming, trace the step-by-step execution path, and fix un
Review a legacy utility module with cryptic variable naming, trace the step-by-step execution path, and fix underlying edge-case bugs under strict time constraints.
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Refactor a monolithic data processing method into modular, testable functions after fixing its primary executi
Refactor a monolithic data processing method into modular, testable functions after fixing its primary execution bugs.
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Walk through a time when a major bug or production incident occurred in your deployed application. How did you
Walk through a time when a major bug or production incident occurred in your deployed application. How did you diagnose, resolve, and prevent it from recurring?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Braze candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Braze loop
- Write out the reported sequence: Recruiter Call, Technical Screen, Virtual Onsite Loop, Code Debugging Session, Behavioral 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 5 reported rounds, with the weakest marked.
02Work Algorithms (general)
- Spend the session on Algorithms (general), which Braze candidates report being tested on.
- Write one worked example in Algorithms (general) and time yourself on it.
Deliverable: One timed worked example in Algorithms (general).
03Work System Design
- Spend the session on System Design, which Braze candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
04Work Debugging
- Spend the session on Debugging, which Braze candidates report being tested on.
- Write one worked example in Debugging and time yourself on it.
Deliverable: One timed worked example in Debugging.
05Answer out loud: Algorithmic & Data Structure Challenges
- Answer aloud, timed: Implement a function to evaluate string pattern matching and handle complex nested parentheses validation with depth constraints.
- Answer aloud, timed: Solve a progressive matrix traversal problem, handling dynamic obstacles and scaling the solution across higher dimensions.
Deliverable: Spoken answers to 2 reported Algorithmic & Data Structure Challenges question(s), under time.
06Answer out loud: Code Debugging & Refactoring
- Answer aloud, timed: Given a pre-packaged codebase containing failing unit tests for a text encryption algorithm, identify the logic errors and pass all tests without altering external interfaces.
- Answer aloud, timed: Review a legacy utility module with cryptic variable naming, trace the step-by-step execution path, and fix underlying edge-case bugs under strict time constraints.
Deliverable: Spoken answers to 2 reported Code Debugging & Refactoring question(s), under time.
07Answer out loud: System Design & Infrastructure
- Answer aloud, timed: Design a high-throughput ad scraping and analytics pipeline capable of ingesting external web data and serving real-time reporting dashboards.
- Answer aloud, timed: Create an architecture for a real-time notification delivery engine that guarantees at-least-once message delivery across multiple mobile and web channels.
Deliverable: Spoken answers to 2 reported System Design & Infrastructure question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Explain a scenario where you received critical technical feedback during a code review and how you adapted you
Explain a scenario where you received critical technical feedback during a code review and how you adapted your implementation as a result.
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?
Reverse your own decision and price the reversal
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
Approach
- State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
- Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
- Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
- Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.
Follow-up
- What in that decision was irreversible, and did you know it was irreversible when you made it?
- How did you tell the people who had already built on top of the original decision?
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
- 01
Explain a scenario where you received critical technical feedback during a code review and how you adapted your implementation as a result.
- 02
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
- 03
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
How difficult are the technical interviews at Braze compared to other tech companies?
The technical bar at Braze is high, particularly around real-world problem-solving and live debugging. While algorithm questions are generally medium-level rather than extreme competitive programming puzzles, the debugging round tests speed, code navigation, and practical execution under time pressure.
Braze Software Engineer candidate reports ↗How should I prepare for the unique live debugging interview?
Practice setting up clean local development environments, running test frameworks via terminal commands, and reading unfamiliar code systematically. Focus on identifying logic flaws quickly through print statements or breakpoints rather than attempting to rewrite the entire codebase.
Braze Software Engineer candidate reports ↗What programming languages am I allowed to use during the interview?
For algorithmic and system design rounds, you can generally use any mainstream language you are comfortable with, such as TypeScript, JavaScript, Python, Java, or C#. However, for role-specific frontend or full-stack positions, exercises using TypeScript or React may be required.
Braze Software Engineer candidate reports ↗How long does the hiring process typically take from initial contact to offer?
The full process typically takes between 2 to 4 weeks, depending on scheduling. Communication from recruiters is generally prompt, with updates following each stage of the loop within a few business days.
Braze Software Engineer candidate reports ↗How hard is the Braze interview?
Candidates most commonly rate Braze interviews as medium, based on 361 reported interviews. About 21% of candidates who interview go on to receive an offer.
Braze Software Engineer candidate reports ↗What topics does Braze test in interviews?
Braze interviews most often cover Problem Solving, System Design, Python, Site Reliability Engineering (SRE), and SQL. The exact emphasis depends on the specific role you apply for.
Braze Software Engineer candidate reports ↗Is Braze a good place to work?
Employees rate Braze 4.2 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.
Braze Software Engineer candidate reports ↗Where is Braze headquartered?
Braze is headquartered in New York, US.
Braze Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Braze Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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