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GreedyGame Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at GreedyGame plays a pivotal role in shaping the future of ad-tech and gaming monetization. You are not just writing code; you are building scalable systems that handle high-concurrency traffic and complex data streams, directly impacting the revenue and user experience for a vast network of mobile games and applications. The work here is characterized by high stakes and technical depth. Whether you are optimizing ad-serving algorithms, refining infrastructure for real-time bidding, or developing seamless integration SDKs, your contributions are immediate and visible. You will be expected to bridge the gap between abstract system design and production-grade execution, working in an environment that values engineering excellence, architectural rigor, and the ability to solve problems under significant technical constraints.

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

Focus areas

  • Golang (Go)
  • React
  • Data Structures

The GreedyGame interview process

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

Recruiter Screen

Initial assessment to evaluate baseline fit for the role.

What to demonstrate

  • Initial assessment to evaluate baseline fit for the role
  • 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 Rounds

Engagement in live coding, project reviews, and discussions with senior staff or the CTO.

What to demonstrate

  • Engagement in live coding, project reviews, and discussions with senior staff or the CTO
  • Depth in Golang (Go)

How to prepare

  • Answer aloud and timed: How do you design an API to be truly generic and extensible for future feature enhancements?
  • Answer aloud and timed: What are the most common performance bottlenecks in high-concurrency systems, and how do you mitigate them?

Final Assessment

Comprehensive evaluation of technical choices and system architecture approach.

What to demonstrate

  • Comprehensive evaluation of technical choices and system architecture approach
  • Depth in Golang (Go)

How to prepare

  • Answer aloud and timed: Given a specific load requirement, how would you design a production-grade system for [a specific task like a Todo-list or Ad-server]?
  • Answer aloud and timed: How do you ensure your code remains maintainable and scalable as project requirements evolve?

Where candidates lose points

Prioritize Code Quality

Even in a time-constrained assignment, prioritize readability and modularity.

Own Your Resume

Be prepared to dive deep into every project you list. If you mention a technology, expect to be grilled on its internal mechanics.

Ask Clarifying Questions

When given an assignment or a design problem, ask questions about scale, traffic, and constraints before you start coding. This demonstrates senior-level thinking.

Do not underestimate the importance of the initial HR or telephonic screening

These are often used to gauge your interest and communication style, which are critical for the collaborative culture at the company.

Questions & practice

How would you handle a scenario with 1 million active goroutines while operating under strict memory constrain

Medium · Technical and Domain Expertise

How would you handle a scenario with 1 million active goroutines while operating under strict memory constraints?

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 the most common performance bottlenecks in high-concurrency systems, and how do you mitigate them?

Medium · Technical and Domain Expertise

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?

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

  1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
  2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
  3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
  4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

  • A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
  • Where does the fingerprint get computed relative to request decompression and the body-size limit?

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?

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?

Can you explain the difference between buffered and unbuffered channels in Golang and when to use each?

Medium · Technical and Domain Expertise

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 design an API to be truly generic and extensible for future feature enhancements?

Medium · Technical and Domain Expertise

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?

Given a specific load requirement, how would you design a production-grade system for [a specific task like a

Medium · System Design and Architecture

Given a specific load requirement, how would you design a production-grade system for [a specific task like a Todo-list or Ad-server]?

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 ensure your code remains maintainable and scalable as project requirements evolve?

Medium · System Design and 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 trade-offs do you consider when choosing between different data structures for a high-throughput service?

Medium · System Design and 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?

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 GreedyGame candidates report.

Day 1 — Map the GreedyGame loop

  • Write out the reported sequence: Recruiter Screen, Technical Rounds, Final Assessment.
  • 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 Golang (Go)

  • Spend the session on Golang (Go), which GreedyGame 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 React

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

Deliverable: One timed worked example in React.

Day 4 — Work Data Structures

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

Deliverable: One timed worked example in Data Structures.

Day 5 — Answer out loud: Technical and Domain Expertise

  • Answer aloud, timed: How would you handle a scenario with 1 million active goroutines while operating under strict memory constraints?
  • Answer aloud, timed: Can you explain the difference between buffered and unbuffered channels in Golang and when to use each?

Deliverable: Spoken answers to 2 reported Technical and Domain Expertise question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

  • Answer aloud, timed: Given a specific load requirement, how would you design a production-grade system for [a specific task like a Todo-list or Ad-server]?
  • Answer aloud, timed: How do you ensure your code remains maintainable and scalable as project requirements evolve?

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

Day 7 — Answer out loud: Behavioral and Project Experience

  • Answer aloud, timed: Walk me through your most challenging technical project; what were the primary constraints, and how did you overcome them?
  • Answer aloud, timed: How do you handle tight deadlines when tasked with building complex features from scratch?

Deliverable: Spoken answers to 2 reported Behavioral and Project Experience question(s), under time.

Behavioural preparation

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

Walk me through your most challenging technical project; what were the primary constraints, and how did you ov

Medium · Behavioral and Project Experience

Walk me through your most challenging technical project; what were the primary constraints, and how did you overcome them?

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 tight deadlines when tasked with building complex features from scratch?

Medium · Behavioral and Project Experience

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 you had to optimize a system that was failing under production load.

Medium · Behavioral and Project Experience

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?