As a Software Engineer at DAZN, you are at the heart of the world’s leading sports streaming platform. Your work directly impacts how millions of fans experience live sports, requiring you to build and maintain systems that handle massive concurrency, low-latency video streaming, and highly available architecture. You aren't just writing code; you are solving complex challenges related to scale, real-time data processing, and distributed systems to ensure a seamless viewing experience under intense load. The role demands a blend of technical depth and product intuition. Whether you are working on the backend infrastructure that powers global broadcasts, optimizing front-end performance for a smooth user interface, or designing scalable microservices, your contributions are critical to DAZN’s growth. You will collaborate with cross-functional teams, including product managers and operations, to deliver features that keep the platform competitive, reliable, and user-centric. ##### Tip DAZN values practical problem-solving over rote memorization. During your interviews, focus on explaining your thought process clearly, even if you are unsure of the exact syntax or a specific framework keyword.
Recruiter Screening
reportedInitial screening to align on your experience and expectations.
What to demonstrate
- Initial screening to align on your experience and expectations
- Depth in Data Structures & Algorithms (DSA)
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 Assessments
reportedAssessment tasks to evaluate your technical skills.
What to demonstrate
- Assessment tasks to evaluate your technical skills
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: What are the key differences between SQL and NoSQL databases, and when would you choose one over the other?
- Answer aloud and timed: Explain core concepts in Operating Systems and Computer Networks (e.g., latency, throughput, TCP/IP).
Machine Coding Tasks
reportedPractical coding tasks to demonstrate your programming abilities.
What to demonstrate
- Practical coding tasks to demonstrate your programming abilities
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: What is the difference between Sharding and Replication in database design?
- Answer aloud and timed: Implement a solution for the Longest Increasing Subsequence problem.
Technical Discussions
reportedIn-depth technical discussions with engineering managers or senior developers.
What to demonstrate
- In-depth technical discussions with engineering managers or senior developers
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Write a function to check if a string is a Palindrome.
- Answer aloud and timed: Given a list of lists (e.g., color: [ORANGE, RED]), how would you map inputs to their respective categories?
Behavioral Fit Assessment
reportedEvaluation of your fit within the company culture and team.
What to demonstrate
- Evaluation of your fit within the company culture and team
- Depth in Data Structures & Algorithms (DSA)
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 fit assessment above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Show Your Thought Process
Always talk through your code. Interviewers want to see how you break down problems and handle constraints.
Know Your Resume
Be prepared to explain every project you listed. If you mention a technology, expect a question about how you used it.
Prepare for Behavioral Rounds
Be ready to share examples of how you handled conflict, technical challenges, or tight deadlines.
Focus on Foundations
If you are a beginner, focus on mastering the basics of JavaScript, Node.js, and databases, as these are frequent evaluation points.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you handle asynchronous operations in JavaScript (Promises, async/await)?
How do you handle asynchronous operations in JavaScript (Promises, async/await)?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Implement a solution for the Longest Increasing Subsequence problem.
Implement a solution for the Longest Increasing Subsequence problem.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Write a function to check if a string is a Palindrome.
Write a function to check if a string is a Palindrome.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Given a list of lists (e.g., color: [ORANGE, RED]), how would you map inputs to their respective categories?
Given a list of lists (e.g., color: [ORANGE, RED]), how would you map inputs to their respective categories?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How would you optimize a given code snippet for better performance or memory usage?
How would you optimize a given code snippet for better performance or memory usage?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Can you explain your approach to handling exceptions and retries in a live coding environment?
Can you explain your approach to handling exceptions and retries in a live coding environment?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How would you handle high concurrency during a major live sports event?
How would you handle high concurrency during a major live sports event?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
What are the key differences between SQL and NoSQL databases, and when would you choose one over the other?
What are the key differences between SQL and NoSQL databases, and when would you choose one over the other?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Keep soft-deleted accounts from blocking re-registration
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
- 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.
- 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.
- 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.
- 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 the Event Loop and the Node.js architecture.
Explain the Event Loop and the Node.js architecture.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Explain core concepts in Operating Systems and Computer Networks (e.g., latency, throughput, TCP/IP).
Explain core concepts in Operating Systems and Computer Networks (e.g., latency, throughput, TCP/IP).
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What is the difference between Sharding and Replication in database design?
What is the difference between Sharding and Replication in database design?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Design an architecture for a 1-minute active e-commerce platform.
Design an architecture for a 1-minute active e-commerce platform.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Explain your strategy for caching and load balancing in a distributed system.
Explain your strategy for caching and load balancing in a distributed system.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you ensure High Availability (HA) and Consistency in your designs?
How do you ensure High Availability (HA) and Consistency in your designs?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Exports duplicate a row range about once a week
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
- 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.
- 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.
- 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.
- 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?
Built from the rounds and topics DAZN candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the DAZN loop
- Write out the reported sequence: Recruiter Screening, Online Assessments, Machine Coding Tasks, Technical Discussions, Behavioral Fit 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 5 reported rounds, with the weakest marked.
02Work Data Structures & Algorithms (DSA)
- Spend the session on Data Structures & Algorithms (DSA), which DAZN candidates report being tested on.
- Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.
Deliverable: One timed worked example in Data Structures & Algorithms (DSA).
03Work Coding Interviews / Problem Solving
- Spend the session on Coding Interviews / Problem Solving, which DAZN candidates report being tested on.
- Write one worked example in Coding Interviews / Problem Solving and time yourself on it.
Deliverable: One timed worked example in Coding Interviews / Problem Solving.
04Work SQL
- Spend the session on SQL, which DAZN candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
05Answer out loud: Technical Fundamentals & Core Subjects
- Answer aloud, timed: Explain the Event Loop and the Node.js architecture.
- Answer aloud, timed: How do you handle asynchronous operations in JavaScript (Promises, async/await)?
Deliverable: Spoken answers to 2 reported Technical Fundamentals & Core Subjects question(s), under time.
06Answer out loud: Coding & Problem Solving
- Answer aloud, timed: Implement a solution for the Longest Increasing Subsequence problem.
- Answer aloud, timed: Write a function to check if a string is a Palindrome.
Deliverable: Spoken answers to 2 reported Coding & Problem Solving question(s), under time.
07Answer out loud: System Design & Architecture
- Answer aloud, timed: Design an architecture for a 1-minute active e-commerce platform.
- Answer aloud, timed: How would you handle high concurrency during a major live sports event?
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Estimate work you have never done and defend the range
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
- 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.
- 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.
- 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.
- 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?
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Tell callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
- 01
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.
- 02
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 03
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Is the interview process difficult?
It is generally considered average to challenging. The focus is on your ability to apply fundamentals to real-world scenarios rather than just solving abstract puzzles.
DAZN Software Engineer candidate reports ↗How much time should I spend preparing?
Dedicate enough time to review your own projects in depth, as these are frequent topics of discussion. Brush up on your core language concepts and basic system design principles.
DAZN Software Engineer candidate reports ↗What is the culture like at DAZN?
DAZN is a fast-paced environment. They value professionalism, technical curiosity, and the ability to work well within a collaborative team. ##### Tip Compensation for senior roles can be highly competitive. When discussing salary, research the market rate for your specific location and be prepared to discuss your expectations clearly based on your value and experience.
DAZN Software Engineer candidate reports ↗How hard are DAZN Software Engineer interviews, based on candidate reports?
That suggests steady preparation without assuming everything will be either very easy or extremely technical from the start.
DAZN Software Engineer candidate reports ↗What are the interview rounds for DAZN Software Engineer roles, and how does the loop run?
The process typically starts with a recruiter screening to align on your experience and expectations. After that, you can expect online assessments, machine coding tasks, technical discussions with engineering managers or senior developers, and a final behavioral fit assessment.
DAZN Software Engineer candidate reports ↗What topics does DAZN test for Software Engineer interviews?
Expect a mix of coding and core systems topics, including Data Structures and Algorithms, coding interviews or problem solving, and SQL. System design and database fundamentals also show up, along with areas like scalability, microservices, and concurrency. The guide also highlights practical problem-solving, including explaining your thought process clearly during the interview.
DAZN Software Engineer candidate reports ↗What should I prioritize when preparing for DAZN Software Engineer interviews?
Prioritize being able to articulate the “why” behind your coding and design decisions, because interviewers probe for depth in your specific tech stack and past projects. For technical prep, focus on core foundations that match the role themes: async behavior, concurrency, SQL and DBMS concepts, and system design for scalability and high availability. For the interview format itself, be ready to start by clarifying requirements, outlining your strategy, and then moving into implementation.
DAZN Software Engineer candidate reports ↗What coding questions are publicly listed for DAZN Software Engineer interviews?
Public sample questions include “String Permutation” and “Missing Minimum in Array.” These align with the broader emphasis on Data Structures and Algorithms and coding interview style problem solving.
DAZN Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01DAZN Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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