Global Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Global, you will design, build, and maintain high-throughput distributed systems and mission-critical services that power large-scale applications worldwide. Engineers in this role work across multi-tenant platform teams, global operations, and core infrastructure groups, solving complex engineering challenges around transaction velocity, platform reliability, data streaming, and cross-border integrations. The impact of a Software Engineer at Global reaches millions of end-users and enterprise customers daily. Whether you are engineering real-time data pipelines, optimizing database querying logic to decrease API latency, or constructing resilient microservices capable of processing tens of thousands of concurrent messages, your code directly influences operational stability and user trust.

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

Focus areas

• System Design
• Coding Challenges
• Python

The Global interview process

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

Phone Screen

Initial phase to evaluate candidate's background and fit for the role.

What to demonstrate

• Initial phase to evaluate candidate's background and fit for the role
• Depth in System Design

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 Interviews

One or more interviews that may include live coding exercises and system design discussions.

What to demonstrate

• One or more interviews that may include live coding exercises and system design discussions
• Depth in System Design

How to prepare

• Answer aloud and timed: Implement a thread-safe cache with key-value eviction policies using standard libraries.
• Answer aloud and timed: How do you detect and break a cycle in a singly linked list?

Behavioral Questions

Discussion around problem-solving strategies and cultural fit within the company.

What to demonstrate

• Discussion around problem-solving strategies and cultural fit within the company
• Depth in System Design

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

Where candidates lose points

Do not jump straight into writing code or sketching system boxes before fully clarifying the problem constraints

Candidates who fail to ask clarifying questions often build solutions for incorrect scale or edge cases.

Structure your coding solutions out loud

Always communicate your thought process before writing code. Outline your brute-force approach first, discuss its time and space complexities, and then present your optimized strategy to the interviewer.

Master the STAR methodology for behavioral rounds

Frame your behavioral answers using Situation, Task, Action, and Result. Clearly articulate your direct contribution using "I" statements rather than speaking generically about team effort.

Focus heavily on database and caching strategies

When asked system design questions regarding web application performance, demonstrate a clear understanding of database indexing, query optimization, connection pooling, and multi-layer caching.

Questions & practice

Explain the easiest and most efficient way to find a target number in a sorted list.

Medium · Coding & Data Structures

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?

Given an array of integers, write an algorithm to find two numbers that sum to a specific target value.

Medium · Coding & Data Structures

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?

Implement a thread-safe cache with key-value eviction policies using standard libraries.

Medium · Coding & Data Structures

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 detect and break a cycle in a singly linked list?

Medium · Coding & Data Structures

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?

Reverse a binary tree or perform an in-order traversal iteratively without recursion.

Medium · Coding & Data Structures

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?

Design a concurrent hash map from scratch, explaining lock distribution and synchronization primitives.

Medium · Low-Level Design & Object-Oriented Progr

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?

Walk through how garbage collection works in your language of choice and how it impacts low-latency applicatio

Medium · Low-Level Design & Object-Oriented Progr

Walk through how garbage collection works in your language of choice and how it impacts low-latency applications.

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?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

1. Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
2. Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
3. Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
4. Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

• Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
• Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

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?

Explain the core principles of Object-Oriented Programming (OOP) and how polymorphism is implemented in Java o

Medium · Low-Level Design & Object-Oriented Progr

Explain the core principles of Object-Oriented Programming (OOP) and how polymorphism is implemented in Java or C++.

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 would you design a modular notification system supporting multiple delivery channels (Email, SMS, Push)?

Medium · Low-Level Design & Object-Oriented Progr

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 are the differences between interfaces and abstract classes, and when would you choose one over the other

Medium · Low-Level Design & Object-Oriented Progr

What are the differences between interfaces and abstract classes, and when would you choose one over the other?

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?

You have a high-traffic web application connected to a relational database; how can this application be sped u

Medium · System Design & Architecture

You have a high-traffic web application connected to a relational database; how can this application be sped up?

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 would you design a distributed rate-limiting service to handle millions of requests per second?

Medium · System Design & 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 how you would architect a real-time event pipeline for processing telemetry data globally.

Medium · System Design & 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?

What strategies do you use to maintain data consistency across distributed microservices?

Medium · System Design & 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?

How do message queues (e.g., Kafka, RabbitMQ) handle backpressure and guarantee message order?

Medium · System Design & 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?

Walk me through an end-to-end software project listed on your resume, detailing the architectural trade-offs y

Medium · Behavioral & Execution

Walk me through an end-to-end software project listed on your resume, detailing the architectural trade-offs you made.

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?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

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

Approach

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

Follow-up

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

Suggested practice checklist

Built from the rounds and topics Global candidates report.

Day 1 — Map the Global loop

• Write out the reported sequence: Phone Screen, Technical Interviews, Behavioral Questions.
• 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 System Design

• Spend the session on System Design, which Global 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.

Day 3 — Work Coding Challenges

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

Deliverable: One timed worked example in Coding Challenges.

Day 4 — Work Python

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

Deliverable: One timed worked example in Python.

Day 5 — Answer out loud: Coding & Data Structures

• Answer aloud, timed: Explain the easiest and most efficient way to find a target number in a sorted list.
• Answer aloud, timed: Given an array of integers, write an algorithm to find two numbers that sum to a specific target value.

Deliverable: Spoken answers to 2 reported Coding & Data Structures question(s), under time.

Day 6 — Answer out loud: Low-Level Design & Object-Oriented Programming

• Answer aloud, timed: Design a concurrent hash map from scratch, explaining lock distribution and synchronization primitives.
• Answer aloud, timed: Explain the core principles of Object-Oriented Programming (OOP) and how polymorphism is implemented in Java or C++.

Deliverable: Spoken answers to 2 reported Low-Level Design & Object-Oriented Programming question(s), under time.

Day 7 — Answer out loud: System Design & Architecture

• Answer aloud, timed: You have a high-traffic web application connected to a relational database; how can this application be sped up?
• Answer aloud, timed: How would you design a distributed rate-limiting service to handle millions of requests per second?

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

Behavioural preparation

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

Tell me about a difficult technical or personal scenario you faced in a project. How did you handle it, and wh

Medium · Behavioral & Execution

Tell me about a difficult technical or personal scenario you faced in a project. How did you handle it, and what could you have done better?

Approach

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

Follow-up

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

What is your biggest professional achievement to date, and what technical role did you play in its delivery?

Medium · Behavioral & Execution

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 situations where product requirements are vague or shift rapidly mid-sprint?

Medium · Behavioral & Execution

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 design decision. How was it

Medium · Behavioral & Execution

Describe a time when you disagreed with a senior engineer or project manager on a design decision. How was it resolved?

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?