As a Software Engineer at LSEG (London Stock Exchange Group), you are at the heart of the global financial infrastructure. You are not just writing code; you are building the high-performance, low-latency, and highly secure systems that power international capital markets. Your contributions directly influence how data is processed, how trades are executed, and how financial institutions across the globe maintain operational resilience. The work here is defined by scale and precision. Whether you are working on real-time market data platforms, cloud-native clearing services, or complex analytics tools, you will be solving problems that require both deep technical rigor and an understanding of the systemic importance of the financial sector. You will collaborate with cross-functional teams of engineers, quantitative analysts, and product experts, ensuring that the technology you deliver is as robust as it is innovative. ##### Tip Candidates who demonstrate a deep understanding of the intersection between financial stability and software performance often stand out during the evaluation process.
Initial Screening
reportedThe process begins with an initial screening to assess candidate qualifications and fit.
What to demonstrate
- The process begins with an initial screening to assess candidate qualifications and fit
- Depth in Software Engineering (General)
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 Assessment
reportedCandidates undergo deep-dive technical assessments to evaluate their technical proficiency.
What to demonstrate
- Candidates undergo deep-dive technical assessments to evaluate their technical proficiency
- Depth in Software Engineering (General)
How to prepare
- Answer aloud and timed: How do you ensure high availability and disaster recovery in a cloud-native architecture?
- Answer aloud and timed: Describe your approach to optimizing a service that is experiencing memory leaks or high latency.
Behavioral Assessment
reportedBehavioral assessments focus on candidates' adaptability and curiosity through past project discussions.
What to demonstrate
- Behavioral assessments focus on candidates' adaptability and curiosity through past project discussions
- Depth in Software Engineering (General)
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 assessment above and write down what you would ask to confirm before it.
Final Decision-Making
reportedThe final stage involves decision-making based on the assessments and interviews conducted.
What to demonstrate
- The final stage involves decision-making based on the assessments and interviews conducted
- Depth in Software Engineering (General)
How to prepare
- Answer aloud and timed: How do you prioritize tasks when faced with conflicting requirements from stakeholders?
- Answer aloud and timed: Tell me about a time you identified a critical production issue before it impacted users.
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
LSEG (London Stock Exchange Group) Software Engineer interview: assessment centre
I began with timed assessment activity and then an online assessment centre running from late morning into the afternoon. It included a group round, a technical interview, and a motivation-focused interview. The questions were mostly standard, but it felt competitive because every part seemed tightly measured against other candidates. The process then had aptitude, coding, and group-discussion co…
Read full experiencePracHub editorial advice for the preparation topics above.
Structure your answers
Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses clear and impactful.
Be transparent about trade-offs
In system design, there is rarely one "right" answer. Focus on explaining the compromises you made and why they were appropriate for the specific context.
Ask meaningful questions
Use the final minutes of your interview to ask about team dynamics, the biggest technical challenges the group is facing, or how the team balances innovation with stability.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Canonicalise a request body into a stable idempotency fingerprint
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
- 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.
- 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.
- 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.
- 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?
Identify the heaviest tenants in a five-minute window under memory pressure
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?
Diff a projection against the primary without per-row point reads
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?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
How would you design a low-latency messaging system for high-frequency trading data?
How would you design a low-latency messaging system for high-frequency trading data?
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 the trade-offs between different consistency models in a distributed database.
Explain the trade-offs between different consistency models in a distributed database.
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 and disaster recovery in a cloud-native architecture?
How do you ensure high availability and disaster recovery in a cloud-native 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 implement secure API communication in a highly regulated environment?
How do you implement secure API communication in a highly regulated environment?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- 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?
Describe your approach to optimizing a service that is experiencing memory leaks or high latency.
Describe your approach to optimizing a service that is experiencing memory leaks or high latency.
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?
Built from the rounds and topics LSEG (London Stock Exchange Group) candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the LSEG (London Stock Exchange Group) loop
- Write out the reported sequence: Initial Screening, Technical Assessment, Behavioral Assessment, Final Decision-Making.
- 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.
02Work Software Engineering (General)
- Spend the session on Software Engineering (General), which LSEG (London Stock Exchange Group) candidates report being tested on.
- Write one worked example in Software Engineering (General) and time yourself on it.
Deliverable: One timed worked example in Software Engineering (General).
03Work System Design
- Spend the session on System Design, which LSEG (London Stock Exchange Group) candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
04Work Programming Languages (General)
- Spend the session on Programming Languages (General), which LSEG (London Stock Exchange Group) candidates report being tested on.
- Write one worked example in Programming Languages (General) and time yourself on it.
Deliverable: One timed worked example in Programming Languages (General).
05Answer out loud: Technical and System Design
- Answer aloud, timed: How would you design a low-latency messaging system for high-frequency trading data?
- Answer aloud, timed: Explain the trade-offs between different consistency models in a distributed database.
Deliverable: Spoken answers to 2 reported Technical and System Design question(s), under time.
06Answer out loud: Behavioral and Leadership
- Answer aloud, timed: Describe a time you had to resolve a technical disagreement within your team.
- Answer aloud, timed: How do you prioritize tasks when faced with conflicting requirements from stakeholders?
Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.
07Dry run for LSEG (London Stock Exchange Group)
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
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.
Describe a time you had to resolve a technical disagreement within your team.
Describe a time you had to resolve a technical disagreement within your team.
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?
How do you prioritize tasks when faced with conflicting requirements from stakeholders?
How do you prioritize tasks when faced with conflicting requirements from stakeholders?
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?
Tell me about a time you identified a critical production issue before it impacted users.
Tell me about a time you identified a critical production issue before it impacted users.
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?
How do you balance the need for rapid delivery with the requirement for long-term code maintainability?
How do you balance the need for rapid delivery with the requirement for long-term code maintainability?
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?
What steps do you take to mentor junior engineers or improve team processes?
What steps do you take to mentor junior engineers or improve team processes?
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?
- 01
Describe a time you had to resolve a technical disagreement within your team.
- 02
How do you prioritize tasks when faced with conflicting requirements from stakeholders?
- 03
Tell me about a time you identified a critical production issue before it impacted users.
- 04
How do you balance the need for rapid delivery with the requirement for long-term code maintainability?
How long does the interview process typically take?
The timeline varies, but most candidates complete the cycle within 3 to 6 weeks. We aim for efficiency while ensuring every candidate has a comprehensive assessment.
LSEG (London Stock Exchange Group) Software Engineer candidate reports ↗What differentiates top-tier candidates?
Successful candidates demonstrate a "growth mindset"—they are eager to learn, they take ownership of their work, and they can clearly articulate the reasoning behind their technical decisions.
LSEG (London Stock Exchange Group) Software Engineer candidate reports ↗Is there a focus on specific technologies?
We prioritize fundamental engineering skills over proficiency in any single tool. If you have a strong grasp of computer science principles, you can succeed regardless of your specific stack.
LSEG (London Stock Exchange Group) Software Engineer candidate reports ↗What topics does LSEG (London Stock Exchange Group) test in interviews?
LSEG (London Stock Exchange Group) interviews most often cover SQL, Problem Solving, Python, Behavioral Interviewing, and Stakeholder Communication. The exact emphasis depends on the specific role you apply for.
LSEG (London Stock Exchange Group) 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.
- 01LSEG (London Stock Exchange Group) 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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