cswork / libraryOpen practice workspace

Interview guides · Feedzai

Feedzai Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Feedzai, you will be at the forefront of the fight against financial crime. Feedzai is a market leader in using artificial intelligence and machine learning to safeguard payment systems, banking transactions, and digital trust across the globe. In this role, you will design, build, and scale highly resilient, low-latency distributed systems that process billions of dollars in real-time transactions daily. Your work directly impacts millions of end-users by preventing fraud and money laundering before they can occur. You will join specialized engineering teams such as Digital Trust, Escudo, Platform Engineering, or Customer Success. Regardless of the specific team, the engineering challenges at Feedzai are distinct: you will work on high-throughput streaming pipelines, complex multi-threaded architectures, and advanced data engines where milliseconds matter.

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

Focus areas

  • Real-time systems
  • Problem solving
  • Algorithms

The Feedzai interview process

Feedzai candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Call

Initial screening call with a recruiter to discuss your background, career goals, and alignment with the role.

What to demonstrate

  • Initial screening call with a recruiter to discuss your background, career goals, and alignment with the role
  • Depth in Real-time systems

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.

Online Assessment

Timed online assessment testing foundational computer science skills.

What to demonstrate

  • Timed online assessment testing foundational computer science skills
  • Depth in Real-time systems

How to prepare

  • Answer aloud and timed: How would you optimize search and retrieval operations on a large-scale, in-memory cache using custom data structures?
  • Answer aloud and timed: Design a data structure that supports insert, delete, and get-random operations in O(1) time.

Technical Interviews

Multiple technical interviews focusing on various technical capabilities.

What to demonstrate

  • Multiple technical interviews focusing on various technical capabilities
  • Depth in Real-time systems

How to prepare

  • Answer aloud and timed: Explain the Java memory model and how the JVM manages thread synchronization and memory visibility.
  • Answer aloud and timed: How do you identify, prevent, and debug a deadlock situation in a production environment?

Hands-on Coding Challenge

Practical coding challenge to evaluate coding skills in a real-time environment.

What to demonstrate

  • Practical coding challenge to evaluate coding skills in a real-time environment
  • Depth in Real-time systems

How to prepare

  • Answer aloud and timed: Implement a thread-safe producer-consumer queue without using Java's built-in concurrent collections.
  • Answer aloud and timed: What is garbage collection tuning, and how does GC latency impact real-time transaction processing?

Conversation with Management

Final discussion with senior engineering management to assess fit and expectations.

What to demonstrate

  • Final discussion with senior engineering management to assess fit and expectations
  • Depth in Real-time systems

How to prepare

  • Answer aloud and timed: Design a real-time stock notification system based on user-defined triggers (e.g., if a stock price drops below a threshold, trigger a specific user-defined action).
  • Answer aloud and timed: How would you design a rate-limiting API that can handle millions of requests per second across a distributed cluster?

Where candidates lose points

Think out loud during technical rounds

Your interviewers want to understand your thought process. Explain your approach, discuss trade-offs, and state your assumptions clearly as you write code or design systems.

If you are interviewing for a junior software engineer role, clarify the expectations of the interview early

If the interview starts focusing heavily on complex API design or system architecture that you have not experienced, politely communicate your background so the interviewers can gauge your core computer science strengths fairly.

Prioritize working code over perfection

In both live coding and take-home challenges, ensure your code compiles and runs successfully first. You can always refactor and optimize once you have a functional baseline.

Questions & practice

Implement an efficient algorithm to traverse a directed graph and detect cycles, explaining the time and space

Medium · Data Structures & Algorithms

Implement an efficient algorithm to traverse a directed graph and detect cycles, explaining the time and space complexity.

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 transactional data, find the longest continuous sequence of transactions that meet a specifi

Medium · Data Structures & Algorithms

Given an array of transactional data, find the longest continuous sequence of transactions that meet a specific fraud signature.

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?

How would you optimize search and retrieval operations on a large-scale, in-memory cache using custom data str

Medium · Data Structures & Algorithms

How would you optimize search and retrieval operations on a large-scale, in-memory cache using custom 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?

Explain the Java memory model and how the JVM manages thread synchronization and memory visibility.

Medium · Concurrency & Multi-Threading

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?

Implement a thread-safe producer-consumer queue without using Java's built-in concurrent collections.

Medium · Concurrency & Multi-Threading

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 is garbage collection tuning, and how does GC latency impact real-time transaction processing?

Medium · Concurrency & Multi-Threading

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?

Talk about a project where you had to work with ambiguous requirements. How did you define the scope and deliv

Medium · Behavioral & Collaboration

Talk about a project where you had to work with ambiguous requirements. How did you define the scope and deliver the solution?

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 database schema for a high-volume payment processing system, explaining your choices between SQL and

Medium · System Design & API Architecture

Design a database schema for a high-volume payment processing system, explaining your choices between SQL and NoSQL databases.

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

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?

Design a data structure that supports insert, delete, and get-random operations in O(1) time.

Medium · Data Structures & Algorithms

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?

Design a real-time stock notification system based on user-defined triggers (e.g., if a stock price drops belo

Medium · System Design & API Architecture

Design a real-time stock notification system based on user-defined triggers (e.g., if a stock price drops below a threshold, trigger a specific user-defined action).

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 rate-limiting API that can handle millions of requests per second across a distributed

Medium · System Design & API Architecture

How would you design a rate-limiting API that can handle millions of requests per second across a distributed cluster?

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 handle schema evolution and backward compatibility in a distributed system with active API endpoint

Medium · System Design & API Architecture

How do you handle schema evolution and backward compatibility in a distributed system with active API endpoints?

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 balance the need for fast feature delivery with maintaining high code quality and architectural sta

Medium · Behavioral & Collaboration

How do you balance the need for fast feature delivery with maintaining high code quality and architectural standards?

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 identify, prevent, and debug a deadlock situation in a production environment?

Medium · Concurrency & Multi-Threading

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. 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?

Suggested practice checklist

Built from the rounds and topics Feedzai candidates report.

Day 1 — Map the Feedzai loop

  • Write out the reported sequence: Recruiter Call, Online Assessment, Technical Interviews, Hands-on Coding Challenge, Conversation with Management.
  • 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.

Day 2 — Work Real-time systems

  • Spend the session on Real-time systems, which Feedzai candidates report being tested on.
  • Write one worked example in Real-time systems and time yourself on it.

Deliverable: One timed worked example in Real-time systems.

Day 3 — Work Problem solving

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

Deliverable: One timed worked example in Problem solving.

Day 4 — Work Algorithms

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

Deliverable: One timed worked example in Algorithms.

Day 5 — Answer out loud: Data Structures & Algorithms

  • Answer aloud, timed: Implement an efficient algorithm to traverse a directed graph and detect cycles, explaining the time and space complexity.
  • Answer aloud, timed: Given an array of transactional data, find the longest continuous sequence of transactions that meet a specific fraud signature.

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

Day 6 — Answer out loud: Concurrency & Multi-Threading

  • Answer aloud, timed: Explain the Java memory model and how the JVM manages thread synchronization and memory visibility.
  • Answer aloud, timed: How do you identify, prevent, and debug a deadlock situation in a production environment?

Deliverable: Spoken answers to 2 reported Concurrency & Multi-Threading question(s), under time.

Day 7 — Answer out loud: System Design & API Architecture

  • Answer aloud, timed: Design a real-time stock notification system based on user-defined triggers (e.g., if a stock price drops below a threshold, trigger a specific user-defined action).
  • Answer aloud, timed: How would you design a rate-limiting API that can handle millions of requests per second across a distributed cluster?

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

Behavioural preparation

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

Describe a time when you had a technical disagreement with a team lead or architect. How did you resolve it, a

Medium · Behavioral & Collaboration

Describe a time when you had a technical disagreement with a team lead or architect. How did you resolve it, and what was the outcome?

Approach

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

Follow-up

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

Tell me about a complex production bug you had to debug under a tight deadline. What was your process?

Medium · Behavioral & Collaboration

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?

Estimate work you have never done and defend the range

Hard · estimation · backfills · expand-contract

You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.

Approach

  1. Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
  2. Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
  3. Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
  4. Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.

Follow-up

  • How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
  • Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?