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bet365 Software Engineer Interview Guide

bet365 Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at bet365, you are at the heart of one of the world’s most sophisticated, high-traffic digital platforms. Your work directly impacts millions of users, requiring you to build and maintain systems that must handle extreme concurrency, low-latency requirements, and 24/7 availability. Whether you are working on the Sports platform, MarTech, or Core Systems, your contributions ensure the reliability and performance of a platform that never sleeps. This role is not merely about writing code; it is about engineering for scale and precision. You will collaborate with cross-functional teams, including product managers and operations, to evolve technology that powers real-time data flow and complex user experiences.

This guide is scoped to a Software Engineer candidate at bet365.

Focus areas

C#
SQL
Solid Principles (SOLID)

The bet365 interview process

bet365 candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Call

Initial screening call with a recruiter to discuss your background and interest in the company.

What to demonstrate

Initial screening call with a recruiter to discuss your background and interest in the company
Depth in C#

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

One or more technical interviews involving high-level discussions, code walkthroughs, or live technical exercises.

What to demonstrate

One or more technical interviews involving high-level discussions, code walkthroughs, or live technical exercises
Depth in C#

How to prepare

Answer aloud and timed: What are the SOLID principles, and can you provide an example of how you applied them in a recent project?
Answer aloud and timed: Describe the difference between various CSS layout techniques for mobile web development.

On-site Visit

Meet the team in person at a regional office to observe the working environment.

What to demonstrate

Meet the team in person at a regional office to observe the working environment
Depth in C#

How to prepare

Answer aloud and timed: How do you approach debugging a performance bottleneck in a web application?
Answer aloud and timed: Walk me through everything that happens during a web request from a user's perspective.

Final Assessments

Technical and behavioral assessments to evaluate fit within the team.

What to demonstrate

Technical and behavioral assessments to evaluate fit within the team
Depth in C#

How to prepare

Answer aloud and timed: How would you design a system to handle high-volume, real-time data updates?
Answer aloud and timed: What are the trade-offs when choosing between different database indexing strategies?

Where candidates lose points

Be Honest About Knowledge

If you do not know the answer to a question, admit it, but follow up by explaining how you would go about finding the answer. This demonstrates a growth mindset.

Prepare Your Projects

Be ready to discuss the technical challenges you faced in your previous roles in detail. Know the "why" behind your technology choices.

Understand the Domain

While you do not need to be a betting expert, having a basic understanding of how bet365 operates can help you frame your technical solutions more effectively.

Practice STAR Method

Even for technical roles, be prepared to use the STAR (Situation, Task, Action, Result) method for behavioral questions to ensure your answers are structured and impactful.

Ask Questions

Use the interview as an opportunity to learn about the team’s challenges. Asking insightful questions about their technology stack or development methodology shows genuine interest.

Questions & practice

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Explain how you would optimize a SQL query for a high-transactional database.

Medium · Technical and Domain Knowledge

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?

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

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.
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.
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.
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?

What are the SOLID principles, and can you provide an example of how you applied them in a recent project?

Medium · Technical and Domain Knowledge

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Describe the difference between various CSS layout techniques for mobile web development.

Medium · Technical and Domain Knowledge

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Walk me through everything that happens during a web request from a user's perspective.

Medium · System Design and 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?

How would you design a system to handle high-volume, real-time data updates?

Medium · System Design and 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?

What are the trade-offs when choosing between different database indexing strategies?

Medium · System Design and 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?

How do you approach designing a service that needs to be highly available?

Medium · System Design and 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?

How do you approach debugging a performance bottleneck in a web application?

Medium · Technical and Domain Knowledge

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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 bet365 candidates report.

Day 1 — Map the bet365 loop

Write out the reported sequence: Recruiter Call, Technical Rounds, On-site Visit, Final Assessments.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work C#

Spend the session on C#, which bet365 candidates report being tested on.
Write one worked example in C# and time yourself on it.

Deliverable: One timed worked example in C#.

Day 3 — Work SQL

Spend the session on SQL, which bet365 candidates report being tested on.
Write one worked example in SQL and time yourself on it.

Deliverable: One timed worked example in SQL.

Day 4 — Work Solid Principles (SOLID)

Spend the session on Solid Principles (SOLID), which bet365 candidates report being tested on.
Write one worked example in Solid Principles (SOLID) and time yourself on it.

Deliverable: One timed worked example in Solid Principles (SOLID).

Day 5 — Answer out loud: Technical and Domain Knowledge

Answer aloud, timed: Explain how you would optimize a SQL query for a high-transactional database.
Answer aloud, timed: How do you handle concurrency issues in Java or.NET environments?

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

Day 6 — Answer out loud: System Design and Architecture

Answer aloud, timed: Walk me through everything that happens during a web request from a user's perspective.
Answer aloud, timed: How would you design a system to handle high-volume, real-time data updates?

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

Day 7 — Answer out loud: Behavioral and Soft Skills

Answer aloud, timed: Tell me about the most difficult project you have worked on and what you learned.
Answer aloud, timed: How do you handle conflict within an engineering team?

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

Behavioural preparation

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

How do you handle concurrency issues in Java or.NET environments?

Medium · Technical and Domain Knowledge

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?