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Thunes Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Thunes, you will be at the center of building the "Smart Superhighway" that moves money around the world. Thunes operates a proprietary Direct Global Network that enables real-time, cross-border payments across more than 130 countries and 80+ currencies. Your work directly impacts how millions of users, global gig economy giants (like Uber and Deliveroo), and major super-apps (like Grab and WeChat) transfer funds instantly and securely. This role is highly critical because the Thunes infrastructure connects to over 7 billion mobile wallets and bank accounts, alongside 15 billion cards globally. To support this massive scale, the engineering team designs, builds, and maintains highly available, low-latency APIs and services.

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

Focus areas

  • Golang (Go)
  • API Design (RESTful APIs)
  • Event-Driven Architecture

The Thunes interview process

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

Recruiter Call

Initial screening call with a recruiter to discuss your background, motivations, and overall experience.

What to demonstrate

  • Initial screening call with a recruiter to discuss your background, motivations, and overall experience
  • Depth in Golang (Go)

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 Screening

A brief video recording or a short technical screening focusing on basic web concepts and security may follow the recruiter call.

What to demonstrate

  • A brief video recording or a short technical screening focusing on basic web concepts and security may follow the recruiter call
  • Depth in Golang (Go)

How to prepare

  • Answer aloud and timed: How do you handle idempotency in API transactions to prevent double-charging or duplicate transfers?
  • Answer aloud and timed: Describe how you would integrate an event-driven architecture using message brokers like RabbitMQ or Apache Kafka to handle asynchronous payment notifications.

Take-Home Assignment

A comprehensive coding assignment requiring you to build a functional API or microservice using Golang or your language of choice.

What to demonstrate

  • A comprehensive coding assignment requiring you to build a functional API or microservice using Golang or your language of choice
  • Depth in Golang (Go)

How to prepare

  • Answer aloud and timed: How do you secure public-facing APIs against common security threats like man-in-the-middle attacks or injection?
  • Answer aloud and timed: Explain how concurrency works in Golang (goroutines and channels) and how you avoid race conditions.

Technical Interviews

Deep-dive technical interviews including presentation of your assignment, live coding sessions, algorithm tests, and system design discussions.

What to demonstrate

  • Deep-dive technical interviews including presentation of your assignment, live coding sessions, algorithm tests, and system design discussions
  • Depth in Golang (Go)

How to prepare

  • Answer aloud and timed: When designing a transactional system, how do you choose between a relational database like PostgreSQL and a NoSQL database?
  • Answer aloud and timed: How do you implement and manage database transactions to ensure ACID compliance during a multi-step payment flow?

Where candidates lose points

Proactively manage your application status

Because the hiring process involves multiple stakeholders across different regions, communication can sometimes experience delays. If you do not hear back within a few days of submitting your take-home assignment, send a polite follow-up email to your recruiter.

Brush up on financial transaction concepts

You do not need to be a fintech expert, but understanding basic payment flows, ledger systems, double-entry bookkeeping, and transaction states will give you a significant advantage during system design discussions.

Optimize for readability over cleverness

During live coding or in your take-home test, prioritize clean, readable, and idiomatic code over overly complex algorithms. Thunes values maintainability and ease of collaboration above all.

Questions & practice

Explain how concurrency works in Golang (goroutines and channels) and how you avoid race conditions.

Medium · Language-Specific & Technical Fundamenta

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

When designing a transactional system, how do you choose between a relational database like PostgreSQL and a N

Medium · Language-Specific & Technical Fundamenta

When designing a transactional system, how do you choose between a relational database like PostgreSQL and a NoSQL database?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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 implement and manage database transactions to ensure ACID compliance during a multi-step payment fl

Medium · Language-Specific & Technical Fundamenta

How do you implement and manage database transactions to ensure ACID compliance during a multi-step payment flow?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

What are some common design patterns you use to keep your codebase clean, readable, and maintainable?

Medium · Language-Specific & Technical Fundamenta

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

  1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
  2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
  3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
  4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

  • The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
  • How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

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

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

How would you design a highly available mobile wallet solution that supports real-time transactions?

Medium · API Design & System Architecture

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 differences between various HTTP methods (GET, POST, PUT, DELETE, PATCH) and when you would use ea

Medium · API Design & System Architecture

Explain the differences between various HTTP methods (GET, POST, PUT, DELETE, PATCH) and when you would use each in an API design.

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 handle idempotency in API transactions to prevent double-charging or duplicate transfers?

Medium · API Design & System Architecture

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?

Describe how you would integrate an event-driven architecture using message brokers like RabbitMQ or Apache Ka

Medium · API Design & System Architecture

Describe how you would integrate an event-driven architecture using message brokers like RabbitMQ or Apache Kafka to handle asynchronous payment notifications.

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 secure public-facing APIs against common security threats like man-in-the-middle attacks or injecti

Medium · API Design & System Architecture

How do you secure public-facing APIs against common security threats like man-in-the-middle attacks or injection?

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?

Describe your approach to writing testable code. What is your strategy for writing unit tests versus integrati

Medium · Language-Specific & Technical Fundamenta

Describe your approach to writing testable code. What is your strategy for writing unit tests versus integration tests in Go?

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?

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

Medium · lock contention · ddl · migrations

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

Approach

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

Follow-up

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

Suggested practice checklist

Built from the rounds and topics Thunes candidates report.

Day 1 — Map the Thunes loop

  • Write out the reported sequence: Recruiter Call, Technical Screening, Take-Home Assignment, Technical 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 4 reported rounds, with the weakest marked.

Day 2 — Work Golang (Go)

  • Spend the session on Golang (Go), which Thunes candidates report being tested on.
  • Write one worked example in Golang (Go) and time yourself on it.

Deliverable: One timed worked example in Golang (Go).

Day 3 — Work API Design (RESTful APIs)

  • Spend the session on API Design (RESTful APIs), which Thunes candidates report being tested on.
  • Write one worked example in API Design (RESTful APIs) and time yourself on it.

Deliverable: One timed worked example in API Design (RESTful APIs).

Day 4 — Work Event-Driven Architecture

  • Spend the session on Event-Driven Architecture, which Thunes candidates report being tested on.
  • Write one worked example in Event-Driven Architecture and time yourself on it.

Deliverable: One timed worked example in Event-Driven Architecture.

Day 5 — Answer out loud: API Design & System Architecture

  • Answer aloud, timed: How would you design a highly available mobile wallet solution that supports real-time transactions?
  • Answer aloud, timed: Explain the differences between various HTTP methods (GET, POST, PUT, DELETE, PATCH) and when you would use each in an API design.

Deliverable: Spoken answers to 2 reported API Design & System Architecture question(s), under time.

Day 6 — Answer out loud: Language-Specific & Technical Fundamentals

  • Answer aloud, timed: Explain how concurrency works in Golang (goroutines and channels) and how you avoid race conditions.
  • Answer aloud, timed: When designing a transactional system, how do you choose between a relational database like PostgreSQL and a NoSQL database?

Deliverable: Spoken answers to 2 reported Language-Specific & Technical Fundamentals question(s), under time.

Day 7 — Answer out loud: Behavioral & Culture Fit

  • Answer aloud, timed: Describe a time when you had to deliver a complex technical feature under a tight deadline. How did you prioritize your tasks?
  • Answer aloud, timed: How do you handle feedback during code reviews, and how do you deliver constructive feedback to your peers?

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

Behavioural preparation

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

Describe a time when you had to deliver a complex technical feature under a tight deadline. How did you priori

Medium · Behavioral & Culture Fit

Describe a time when you had to deliver a complex technical feature under a tight deadline. How did you prioritize your tasks?

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 feedback during code reviews, and how do you deliver constructive feedback to your peers?

Medium · Behavioral & Culture 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?

Why are you interested in the fintech industry, and how do you stay updated with modern aspects of your tech s

Medium · Behavioral & Culture Fit

Why are you interested in the fintech industry, and how do you stay updated with modern aspects of your tech stack?

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 experience where a production service failed or degraded. How did you triage, resolve, and prevent th

Medium · Behavioral & Culture Fit

Share an experience where a production service failed or degraded. How did you triage, resolve, and prevent the issue from happening again?

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?