As a Software Engineer at Barclays, you are at the core of building, scaling, and securing the technology that powers a global financial institution. Barclays processes millions of transactions daily across consumer banking, corporate and investment banking, foreign exchange (FX), and wealth management. In this role, you will design and implement mission-critical applications where high availability, ultra-low latency, and robust risk control are fundamental business requirements rather than optional enhancements.
Your technical contributions directly impact millions of retail customers, global corporate clients, and institutional traders. Whether you are engineering low-latency trading engines in C++ or Java, architecting resilient microservices using Spring Boot and AWS, optimizing high-throughput data pipelines using PySpark and Snowflake, or developing secure Customer Identity and Access Management (CIAM) platforms, your code underpins the operational integrity of the bank.
The environment at Barclays balances rapid modern software engineering practices with rigorous financial governance. You will work in cross-functional engineering pods alongside product managers, quantitative developers, and risk officers to solve complex technological challenges. Expect an environment where engineering excellence, secure coding standards, and alignment with corporate values are evaluated with equal weight.
Automated Online Assessment
reportedMost of the time lost in this format is not lost to thinking. It goes to a standard-library call you half-remember, an off-by-one in a loop bound, and a debugging loop that mutates code at random until something passes. When output is wrong, stop re-reading the whole function: take the smallest input that reproduces it and walk the state through by hand, printing intermediates if the environment allows. Guessing at a fix without a failing case you understand is how a five-minute bug becomes twenty, and the clock does not pause while you do it.
What to demonstrate
- Whether you reach the right structure without a detour, and can write it from memory rather than only recall that one exists
- Whether overflow is considered where the language has fixed-width integers, since a signed 32-bit value stops at 2,147,483,647 and then wraps in Java, is undefined behaviour in C++, and does not arise in Python, whose integers grow instead
- Whether recursion depth is treated as a constraint on large inputs, given that CPython's default limit is 1000 frames and a deep recursion can exhaust the stack in any language where an iterative version would not
- Whether a failing case is isolated and explained before any edit is made to the code
How to prepare
- From an empty file and with no references open, implement the pieces you lean on most: a heap push and pop, an iterative DFS with an explicit stack, and a binary search whose midpoint is written lo + (hi - lo) / 2, which avoids the overflow that (lo + hi) / 2 can hit in a fixed-width integer type
- Time yourself on the ten library calls you look up most, such as sorting with a custom comparator, splitting and joining strings, and finding the next key at or above a value in an ordered map, until the lookup is gone
- Take a solution you know is broken and, before touching it, write one sentence naming the input, the expected value and the actual value. Repeat until you do it without deciding to.
Structured Technical Interviews
reportedThe same problem is scored by two different mechanisms depending on the format, and preparing for one does not cover the other. With a person watching, partial progress is visible and a hint is a correction you can absorb; silence is the expensive failure, because nobody can read a half-written function. With an automated grader there is no partial credit for what you were about to do, nobody to ask, and the worked examples in the prompt are the entire specification. Read them as a contract, down to whether an empty result should be an empty list or no output at all.
What to demonstrate
- In a live session, whether your commentary tracks what your hands are doing, and whether a hint redirects you or gets defended against
- In an automated one, whether you cover the cases the examples do not show, since the hidden cases are where the score moves
- Whether you manage the clock on purpose: abandoning an approach that is not converging while there is still time to write something simpler that finishes
How to prepare
- Have someone hand you a problem and feed you one deliberately wrong hint. Practise testing it against a concrete case instead of accepting or rejecting it on authority.
- Do one timed run a week in a plain browser editor with autocomplete, linting and your own snippets switched off, which is closer to what these environments give you
- For the automated format, write the harness before the solution: a main that feeds the worked examples plus an empty and a single-element case and prints expected against actual, so a wrong submission is caught by you first
Multi-part Evaluation
reportedInput bounds are the part of the prompt most often skimmed, and they usually contain the answer. They tell you which complexity class is admissible, which narrows the search before you have thought about the problem itself. As a rough planning figure, a compiled language does on the order of 10^8 simple operations per second and an interpreted one roughly an order of magnitude less. So n up to about twenty admits enumerating subsets, a few thousand admits a quadratic pass, and a million admits neither: you need near-linear, or linear with a log factor. If the bounds are missing, ask for them.
What to demonstrate
- Whether the approach is justified by the stated input size rather than by whichever pattern you recognised first
- Whether you ask about the properties that change the algorithm: whether the input arrives sorted, whether duplicates occur, whether values are bounded integers, whether it all fits in memory
- Whether you can name the bottleneck in your own solution and what would remove it, even when you deliberately leave it in place
- Whether a claimed speedup is real, since memoising a recursion only helps when subproblems genuinely overlap and the state can be keyed cheaply
How to prepare
- For each algorithm you rely on, write down the largest n it handles in roughly a second, then check two of those figures by timing them in the language you will actually type in
- For two weeks, write one line naming your target complexity and the bound that justifies it before you write any code, then compare that line with what you ended up submitting
- Practise the conversion backwards: given a required O(n log n), list the mechanisms that get you there (sorting, a heap, an ordered map, divide and conquer) and choose by what the problem needs to query, not by what you used last
PracHub editorial advice for the preparation topics above.
Treating a timed-out write as a failed write
A timeout says the response did not arrive, not that the work did not happen; the server may well have committed and then lost the connection. Retrying a non-idempotent create after a timeout is the standard way to end up with two of something, and those duplicates land precisely when the system is already degraded and least able to absorb them. The discipline is to treat a timeout as unknown: either the write carries an idempotency key so the retry is safe by construction, or the client re-reads authoritative state before deciding what to do, and the interface says unknown rather than showing a failure that invites a second click.
Paginating a growing table with limit and offset
Two unrelated defects share the idiom. Correctness: rows inserted or deleted between page requests shift the window, so a consumer walking an export skips rows and sees others twice, which for a customer-facing sync is silent data loss rather than an error anyone notices. Cost: the database still produces and discards the skipped rows, so page N costs time proportional to N times the page size and a deep page on a large table degrades from milliseconds to seconds. Keyset pagination over a stable, unique, indexed ordering -- where (created_at, id) < ($1, $2) order by created_at desc, id desc limit $3 -- is constant-cost per page and immune to shifting, on the precondition that the cursor columns never change value for a row, which disqualifies updated_at as a cursor.
Designing for a scale nobody asked for
Ask for request rate, data size, read-to-write ratio and expected growth, then size the simplest option first; one relational instance on current hardware covers a large share of real workloads. Reaching for shards, queues and a cache tier before any number has been quoted reads as pattern-matching rather than judgement.
Listing technologies instead of trade-offs
Name the property the design needs first, such as ordered range scans, multi-entity transactions, cheap appends, or a predictable p99, then pick something that provides it and say what it gives up in exchange. Almost any component is defensible once you state the requirement it satisfies and the one it sacrifices.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given an array of integers and a target weight, solve an Unbounded Kna…
Given an array of integers and a target weight, solve an Unbounded Knapsack problem using dynamic programming and a greedy approach to maximize packed items.
Approach
- Walk one small example through your approach before writing the whole thing.
- Name the brute-force solution and its complexity before improving on it.
- Restate the input: its shape, its size, and what is guaranteed about it.
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 program to reverse a string in Java or Python using basic tech…
Write a program to reverse a string in Java or Python using basic techniques and discuss the most optimal memory approach.
Approach
- Name the brute-force solution and its complexity before improving on it.
- State the target complexity and say which constraint rules the naive version out.
- Walk one small example through your approach before writing the whole thing.
Follow-up
- What is the worst case, and how likely is it on real data?
- Which test case would catch an off-by-one here?
How would you implement a simple string manipulation simulation or pat…
How would you implement a simple string manipulation simulation or pattern printing problem efficiently?
Approach
- State the target complexity and say which constraint rules the naive version out.
- Name the brute-force solution and its complexity before improving on it.
- Walk one small example through your approach before writing the whole thing.
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?
Explain the insertion process in a Binary Search Tree (BST) and compar…
Explain the insertion process in a Binary Search Tree (BST) and compare its operations against a balanced tree structure.
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.
Follow-up
- Which test case would catch an off-by-one here?
- What is the worst case, and how likely is it on real data?
What is the difference between multithreading and concurrency, and how…
What is the difference between multithreading and concurrency, and how does the execution engine handle context switching?
Approach
- Distinguish a value from a reference to it, and say which one you handed out.
- Identify the window where an invariant is briefly untrue.
- Reach for the cheapest primitive that closes the race, not the broadest lock.
Follow-up
- Where could this allocate more than you expect?
- What happens if two callers reach this at the same time?
Explain the key differences between `StringBuffer` and `StringBuilder`…
Explain the key differences between StringBuffer and StringBuilder in Java, particularly regarding thread safety.
Approach
- Reach for the cheapest primitive that closes the race, not the broadest lock.
- 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
- Where could this allocate more than you expect?
- What happens if two callers reach this at the same time?
Fold a deduplicated usage stream into hourly rollups
You are given one day of usage_event rows, up to 250 million, each carrying event_id, tenant_id, workspace_id, environment, sku, quantity numeric(20,6), idempotency_key, occurred_at and ingested_at. Produce usage_rollup_hourly cells keyed (tenant_id, workspace_id, sku, hour_start) with quantity_sum, event_count and source_max_ingested_at. An event counts once per (tenant_id, idempotency_key). The rollup grain has no environment column, so state your filter. One pass. Give your time and space bounds, and say what the deduplication actually costs in memory.
Approach
- Bucket on
occurred_at, neveringested_at:hour_start = date_trunc('hour', occurred_at at time zone 'UTC'). The two columns answer different questions.occurred_atsays which hour the customer is billed for;ingested_atsays how current the fold is. Using the second for the first makes late data invisible instead of correctable. - The fold is trivial and the deduplication is the entire cost, so price it before designing anything clever. An exact set over
(tenant_id, idempotency_key)at 250M entries, stored as a 16-byte 128-bit hash in an open-addressed table at 0.7 load factor, needs about 357M slots at 16 bytes each, roughly 5.7 GB. The fix is partitioning byhash(tenant_id) % Pso each shard holds 1/P of the set and no tenant's keys straddle shards. - Rule out a Bloom filter as a replacement, in the right direction: a false positive reports 'already seen' for an event never seen, so you drop a real event and lose revenue with no error raised. It is usable only as a negative pre-filter in front of the exact set, where a miss is conclusive and a hit must fall through to the real lookup.
- Accumulate in scaled integers, not binary floating point.
numeric(20,6)admits values below 10^14, so one event scaled to micro-units can reach 10^20, past int64's 9.22 x 10^18; use a 128-bit or arbitrary-precision accumulator unless you first bound the per-event maximum. binary64 represents integers exactly only to 2^53, about 9.01 x 10^15, and cannot represent 0.1 at all, so two runs that sum in different orders disagree. - Carry
source_max_ingested_at = max(ingested_at)over the events folded into each cell, and countevent_countover accepted, post-dedup events. Without that watermark there is no way to prove later what a number did and did not include, which is the first question any reconciliation asks. - State the environment filter explicitly, because the rollup grain cannot record it. A fold that quietly includes
stagingbills non-production traffic; one that quietly excludes it loses a cost signal. Production-only is the billing answer, and either way it belongs in the job name and the output metadata. Complexity: O(n) time, O(distinct dedup keys) space, dominated by the dedup set rather than by the cells.
Worked solution 25 min
- Write both key tuples down before any code: dedup key
(tenant_id, idempotency_key), cell key(tenant_id, workspace_id, sku, hour_start), withhour_startderived fromoccurred_atin UTC. - Build a 10,000-row fixture containing one event duplicated three times under the same
idempotency_key, two events sharing anidempotency_keyacross differenttenant_idvalues, one event whoseoccurred_atis two hours before itsingested_at, and onestagingevent inside an otherwise production cell. - Fold it and assert each of those four expectations separately rather than eyeballing a grand total.
- Re-run with the input shuffled and diff the output files.
- Size the dedup set for 250M keys using the load-factor arithmetic and write the number down next to the fixture.
Follow-up
- A producer retries at 23:59:59 and the retry lands at 00:00:01. The unique index on the daily-partitioned table must include the partition key. What gets double-counted, and what is the smallest change that fixes it?
- The consumer acknowledges its batch before committing the fold. Which failure loses revenue now, and which arrangement duplicates instead?
- What makes a re-run over the same day produce byte-identical rollups?
Model credential revocation so history survives the delete
tenant_api_key stores key_id, tenant_id, workspace_id, name, key_prefix, secret_hash, scopes text[], status (active, revoked, expired, compromised), auth_version, created_at, expires_at, last_used_at, revoked_at, revoked_reason. Rotation inserts a new row and revocation never deletes, because an incident review asks which credential served a request last quarter. Write the constraints that enforce: a label is unique only among a tenant's live keys, revoked_at and status can never disagree, and scopes is never empty. Then write the authentication lookup predicate, and name one column in this table that must stay out of it.
Approach
- Reach for a partial unique index rather than a plain UNIQUE:
create unique index on tenant_api_key (tenant_id, name) where revoked_at is null. Any number of revoked rows may share a label, the live namespace stays unique per tenant, and the revoked majority is not in the index at all, so it stays small on a table that only grows. - Tie the nullable timestamp to the enum so the two cannot drift:
check ((revoked_at is not null) = (status in ('revoked','compromised')))andcheck ((revoked_at is null) = (revoked_reason is null)). A revocation that records no reason is the one an incident review cannot use. - Write the emptiness check as
check (cardinality(scopes) > 0), notarray_length(scopes, 1) > 0. array_length returns NULL for an empty array, a CHECK constraint passes when its expression is NULL, so the array_length version accepts exactly the value it was written to reject. - Make the lookup a single index probe with every liveness condition inside it:
where secret_hash = $1 and revoked_at is null and (expires_at is null or expires_at > now()) and auth_version = $2, backed by a unique index on secret_hash. Nothing is filtered in application code, so there is no path that forgets a clause. - Keep last_used_at out of that predicate. It is written asynchronously and is allowed to lag by a minute, so it is a usage signal; feeding it into an authorisation decision makes the decision depend on a write that may be late, batched away or lost.
- Flag the modelling smell while you are here:
expiredis derivable fromexpires_at < now(), so storing it as a status obliges a job to keep it true and guarantees the column is wrong between the expiry instant and that job's next run. Derive it in the predicate; keep the stored status for states that are decisions rather than clock readings.
Follow-up
- Rotation issues a replacement while the old key stays live for a 30-day overlap. What does the uniqueness rule become, and what does the UI show to tell two same-named keys apart?
- A password reset bumps the principal's auth_version. No row in this table changed. How does the next request fail, and what query counts how many keys that bump just killed?
- A key turns up in a public repository. Which columns let you find it, and what do you write to the row?
Enforce a concurrent-run quota that survives simultaneous requests
A plan allows at most 20 concurrently running rows in job_run per tenant. The table holds run_id, tenant_id, workspace_id, status (queued, leased, running, succeeded, failed, timed_out, cancelled, lost), lease_token, leased_until, started_at and finished_at. Today the service runs select count(*) from job_run where tenant_id = $1 and status = 'running', compares the result to 20, then inserts. Under load a tenant exceeds the cap by exactly the number of concurrent requests. Name the anomaly, say which isolation levels do and do not prevent it, and give a version that holds, as SQL.
Approach
- Name it: write skew. Each transaction reads a predicate (the count of running rows), neither modifies what the other read, and both then insert rows that jointly violate an invariant no single row expresses. Read committed permits it. So does repeatable read, because snapshot isolation's first-updater-wins check fires only on conflicting row updates, and these are inserts touching disjoint rows.
- Enumerate the fixes with their real costs. SERIALIZABLE works: PostgreSQL's SSI tracks the predicate read and aborts one transaction with SQLSTATE 40001, which obliges the caller to retry and makes the abort rate rise with contention on a hot tenant. Folding the predicate into the write as
insert ... select ... where (select count(*) ...) < 20narrows the race to the statement's snapshot but does not close it under read committed. - Give the version that holds at read committed: serialise on a row both transactions must touch.
update tenant_concurrency set running = running + 1 where tenant_id = $1 and running < 20 returning runningupdates zero rows when the cap is reached, and zero rows is the rejection. This works because at read committed a blocked UPDATE re-evaluates its WHERE clause against the newly committed row; at repeatable read the same statement raises a serialisation error instead, so the isolation level changes the calling contract. - State the cost you just bought. That row is now a per-tenant serialisation point, so admission throughput for the tenant is bounded by one divided by the lock hold time; at a 2 ms hold that is roughly 500 admissions/second. Keep the critical section to the single UPDATE, with no network call or scheduling decision inside the transaction, and decrement in the same transaction that writes the terminal status.
- Close the leak the status enum implies: a run can end as
lost, so a crashed worker otherwise consumes a slot forever. Reconcile on a schedule againststatus = 'running' and leased_until < now(), and treat the counter as a fast path overjob_run, which stays the system of record.
Worked solution 25 min
- Seed a tenant with 19 running rows, then fire 8 concurrent sessions each running the select-then-insert, and count the resulting running rows.
- Repeat at REPEATABLE READ and confirm the count still exceeds 20.
- Repeat at SERIALIZABLE, count the 40001 aborts, and note that without a retry loop those requests fail rather than queue.
- Implement the atomic counter UPDATE, re-run the 8-way test, and confirm exactly 20 running rows with zero over-admissions.
- Kill a worker mid-run, let the lease expire, and check whether the slot comes back without intervention.
Follow-up
- Write the retry loop for the SERIALIZABLE version. What does the caller see when it keeps aborting, and what bounds the retries?
- Two regions each keep a counter. What is the effective cap, and what does admission do when the counter store is unreachable?
- The cap changes mid-flight on a plan upgrade. Do running jobs get killed, and what does the counter row look like during the change?
What is the difference between a crossover network cable and a straigh…
What is the difference between a crossover network cable and a straight-through cable in network hardware?
Approach
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
- Name the failure you are designing for, then the recovery path.
Follow-up
- What breaks first when traffic grows ten times?
- What would you drop to keep the system up under load?
How do microservices handle service discovery and circuit breaking in …
How do microservices handle service discovery and circuit breaking in containerized environments like Kubernetes?
Approach
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- Fix the scope first: who calls this, how often, and what they do when it fails.
Follow-up
- What would you drop to keep the system up under load?
- What breaks first when traffic grows ten times?
What is the functional difference between a Stored Procedure and a Tri…
What is the functional difference between a Stored Procedure and a Trigger, and when should each be avoided?
Approach
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Design the batch ingest endpoint metering agents retry into
A customer-run agent posts usage events in batches of up to 1,000 to metering-ingest with a 30-second timeout and at-least-once retry of the whole batch. Each event carries event_id, idempotency_key, sku, quantity and occurred_at; the server adds ingested_at, and usage_event is partitioned daily on ingested_at with unique (ingested_day, tenant_id, idempotency_key). Design the endpoint: the request shape, what the response says when 900 events are new, 90 are duplicates and 10 are malformed, the status code, and the agent's algorithm on timeout. Then state the deduplication horizon and justify it against that unique constraint.
Approach
- Fix the per-item outcome taxonomy first, because the status code follows from it: accepted, duplicate, and rejected with a permanent code. A duplicate is a success; reporting it as an error makes the agent either re-send revenue it already delivered or drop it.
- Allow only permanent failures per item. A transient per-item failure inside a 200 invites the agent to discard that event, so anything transient escalates to a 5xx for the entire batch. A 200 is then a promise that every event not marked rejected is committed and durable.
- Return 200 with a results array aligned by index and carrying the event id, so the agent can retry precisely the subset that needs it and quarantine the ten malformed events instead of hard-looping a poison batch forever. Cap the batch at 1,000 items and a byte size, with 413 beyond it and 429 with Retry-After for backpressure.
- Deduplicate per event, never per batch: the agent may split, merge or reorder a retried batch, so a batch-level key matches nothing on the second attempt. The key is (tenant_id, idempotency_key), and the tenant comes from the resolved credential; a tenant id present in the body is compared against it, never trusted.
- Size the horizon as a correctness parameter. The unique index includes the partition key, so it deduplicates only within one day: a retry that crosses midnight, or a replay run a week later, passes straight through it. A separate dedup store keyed (tenant_id, idempotency_key) with a TTL exceeding the agent's maximum retry window plus the longest replay you intend to support is what actually enforces the invariant, which makes its retention a correctness setting rather than a cost knob.
- Order the commit against the response and the acknowledgement: commit then respond at the endpoint, and downstream commit the fold then acknowledge the message. Acknowledging first turns a crash into silently lost revenue with no error raised anywhere.
Worked solution 40 min
- Write the request body schema with the batch envelope and one event, and state which fields the server assigns rather than accepts.
- Write the 200 response for the 900/90/10 case, showing three result entries, one of each outcome, with the rejected one carrying a permanent code.
- Write the rule separating per-item rejection from whole-batch failure, and list which conditions fall on each side.
- Compute the dedup horizon from the agent's retry window plus the replay window you support, and say where the dedup state lives and how it ages out.
- Write the agent's pseudocode for timeout, 5xx, 429 and 200-with-rejections, four branches, and mark which branch may drop an event.
- Trace the crash between commit and response, and between fold and acknowledgement, and say what each produces.
Follow-up
- The agent times out at 30 seconds having received nothing. What exactly does it do next, and what in your design makes that safe?
- Ten events are rejected every hour for a week and nobody notices. What does the endpoint owe the customer beyond a per-item 4xx code?
- A replay pushes 40 million events through this endpoint in an hour. Which part of your design degrades first?
Invoice detail latency triples after an ORM relationship refactor
An invoice detail endpoint returned in 40 ms at p99 last week. After a refactor replaced a hand-written join with ORM relationship access it returns in 1.4 s, and the regression grows with the number of invoice_line_item rows on the invoice. Database CPU rose, but no statement in the slow-query log exceeds 3 ms. You have request traces with per-span SQL, the ORM statement log, and a staging copy of the data. Produce an ordered diagnostic checklist, the measurement that confirms the cause before any code change, and the fix.
Approach
- Count statements per request before reading any statement duration. A slow-query log hides this class by construction, because every individual query is fast and only their number is wrong; take one trace and count SQL spans.
- Establish proportionality rather than asserting it: sample invoices with 5, 20, 60 and 200 line items and plot statements per request against line count. A straight line of slope 1 through an intercept of one or two identifies a lazy relationship load, and no index or cache would move that line.
- Locate the emitting attribute access in the refactored code and check whether the same shape repeats one level deeper, for instance a tax or adjustment collection hanging off each line, which turns the cost quadratic.
- Fix with a bounded statement count: either one join that fetches invoice and lines together, or two statements where the second is WHERE invoice_id = $1 AND tenant_id = $2. Keep tenant_id in the predicate so the read stays tenant-scoped even though invoice_id already implies it.
- Choose between the two deliberately: the join duplicates the wide parent row across N children on the wire, the two-statement form avoids that for one extra round trip. Prefer the join for narrow parents and the split for wide ones.
- Pin it with a per-request statement-count assertion in a test that varies line count, because a latency assertion passes on a small fixture and would not have caught this.
Follow-up
- The endpoint now also needs per-line tax rows. Show the shape that keeps statement count constant instead of reintroducing the same defect one level down.
- How does this change if a transaction-pooling proxy sits between the service and the database, so each statement may land on a different backend session?
- The same page paginates invoices with LIMIT and OFFSET. Why is that a second, independent defect, and what replaces it?
Roughly ninety minutes on weeknights with one longer weekend block. The plan cuts scope rather than compressing everything, on the assumption that one thing finished per night beats four half-started.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and take a cold baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list and keep it visible all week.
- Take one twenty-five-minute coding problem and one fifteen-minute design prompt cold, and write the single sentence naming what blocked each, because those two sentences decide where the remaining evenings go.
- Set the week's rule: one thing finished every night, including the night you only have forty minutes.
Deliverable: A one-page scope with a not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One pattern, written three times from blank
- Choose the single pattern most likely to appear in your loop and write it three times from an empty file rather than editing the previous attempt.
- On the third pass, write the invariant as a comment before the loop body and the complexity before the first line of code.
- Stop at ninety minutes even if the third version is imperfect, and write the one thing you would fix given another hour.
Deliverable: Three independent implementations of the same pattern plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03One design, only to the depth you can defend
- Take one system shape and go only as far as requirements, interface and data model, refusing to draw a box you could not survive a follow-up about.
- Attach one number to each non-functional requirement, deriving it rather than asserting it, and write the assumption the number rests on.
- Write the one tradeoff you are choosing against and the observation that would make you reverse it.
Deliverable: One design at interface-and-schema depth with derived numbers and one written reversible tradeoff.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Only the fundamentals you will have to defend
- Write, in under two hundred words each, the answers to the two questions that follow almost any implementation: why this structure and not the obvious alternative, and what happens to this code at a hundred times the input.
- Write what an index actually costs: faster lookups on the indexed columns against a write that now maintains a second structure, plus the cases where the planner declines to use it anyway, low selectivity, or a predicate wrapping the column in a function.
- Delete any answer you cannot deliver aloud in under a minute, since an answer that needs reading is not an answer you have.
Deliverable: Three written answers, each under two hundred words and each timed aloud.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a ninety-second and a four-minute version of your main project and time both aloud rather than reading them.
- Prepare the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be ready to say exactly where it came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in the weekend block
- Run a sixty-minute mock covering a coding round and a design round in one sitting with no break, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the twenty-minute warm-up you will actually do on the morning: one problem you can already solve from a blank file, one design you can narrate, and nothing you have never seen.
- Re-read only your own notes from this week and open no new material.
- Write the logistics down: the editor or shared document you will be working in, whether execution and lookups are permitted, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the design structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
A slipped date is only a bad story if you sat on it. What matters is what you believed when you gave the number, the signal that told you it was wrong, how many days passed before you said so, and what you cut rather than asking for more time. Scope you defended counts as much as scope you dropped.
Give an example of a project where you had to balance strict security …
Give an example of a project where you had to balance strict security requirements with rapid feature delivery.
Approach
- Pick a story where you made the decision, not one where you watched it.
- 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?
- What did you decide not to do, and why?
Resolve a review disagreement over a quota check
A colleague's pull request enforces a per-tenant quota by selecting the current count and then inserting when it is under the limit. You flag it as a race. They reply that the transaction already runs at repeatable read, so the snapshot makes it safe, and the tests pass. Walk through taking that disagreement to a resolution: what you write in the review, what you demonstrate rather than assert, which fix you propose and why, and what you do if they still disagree after all of it.
Approach
- Answer the claim precisely instead of restating your objection, because they have made a specific technical argument. In PostgreSQL, repeatable read is snapshot isolation; this is write skew, which snapshot isolation permits by design. Both transactions read a count that is stable within their own snapshot, insert disjoint rows that the other cannot see, and both commit, so the limit is exceeded by exactly the concurrency.
- Demonstrate rather than cite. Two psql sessions, both BEGIN ISOLATION LEVEL REPEATABLE READ, both select the count, both insert, both commit: it succeeds. Repeat at SERIALIZABLE and the second commit fails with serialization_failure, SQLSTATE 40001. That takes two minutes, ends the argument without anyone conceding a position, and leaves an artefact for the next reviewer.
- Offer the options with their costs rather than a verdict. Serialisable plus a retry loop on 40001 is correct but obliges every caller to retry and degrades under contention. An increment-and-compare on a counter row — update tenant_quota set used = used + 1 where tenant_id = $1 and used < limit returning used — is safe even at read committed, because a blocked updater re-evaluates the WHERE clause against the row version it finally locks, and zero rows returned means full. A unique or exclusion constraint that makes the surplus write fail is the third.
- Name the plausible non-fix explicitly, since it is what usually gets merged instead: folding the count into the insert as insert ... select ... where (select count(*) ...) < limit is still racy under read committed, because the subquery cannot see the other transaction's uncommitted rows. It looks atomic and is not.
- Say what you do if they still disagree: escalate the decision rather than the disagreement. Attach the reproduction, hand it to the service owner or a third reviewer, and state that you will not block the merge if the owner accepts the risk knowingly — and that you want that acceptance written down.
- Close with the general lesson worth leaving in the review thread: a passing suite is weak evidence for a concurrency claim because it runs one request at a time. Ask for a test that runs two.
Follow-up
- Write the counter-row version. Does your answer change if the quota counts child rows rather than a column?
- Under serialisable, who performs the retry, and what does the API client see if the retry also fails?
- This is the third disagreement with the same reviewer this month. What changes in how you review?
Estimate a tenant-leading index migration you have never run
Someone needs a date. usage_event carries an index on (occurred_at) and needs (tenant_id, occurred_at); the largest tenant holds roughly a hundred times the median tenant's rows, the table is partitioned daily with years of retention, and you have never run a migration on a table this large. Give an estimate you would defend: how you decompose the work, the two or three numbers you would go and measure first, the range and confidence you state, and what you commit to when the person asking needs a single date today.
Approach
- Refuse the bare number and then give one anyway, in the form that is actually useful: a range plus the measurement that collapses it. 'Four to eleven days; one afternoon building this index on a restored copy of the largest partition takes that to within a day' is an answer, while 'it depends' is not.
- Decompose by failure mode rather than into equal chunks, because that is where estimates go wrong. On a partitioned parent you create the index ON ONLY the parent, build each partition's index with CREATE INDEX CONCURRENTLY, then ALTER INDEX ... ATTACH PARTITION, at which point the parent index becomes valid. CONCURRENTLY does not block writes but scans each partition twice, waits out older transactions, cannot run inside a transaction block, and on failure leaves an invalid index you must drop concurrently and retry.
- Name the two unknowns that dominate and price them: build time on one restored partition of realistic size, and whether the planner actually chooses the new index for the skewed tenant, since selectivity for a tenant holding most of the rows is a different question from selectivity for the median tenant. Both are half-day measurements against a replica, and both are cheaper than being wrong by a week.
- State the assumptions the range is conditional on, because that is what makes a slip a re-estimate instead of a credibility event: no partition above a stated row count, one concurrent build at a time so it does not compete with ingest for I/O, and an ingest backlog that can absorb the added write amplification while both indexes exist.
- Budget the step nobody budgets: verification and the old index's removal. Dropping the old index is fast, but deciding it is safe to drop means confirming no plan still uses it, and that confirmation waits on real traffic across a full weekly cycle rather than on your patience.
- Answer the single-date request honestly. Commit to a date for the first checkpoint — the measured build number from the replica — and to re-estimating on that date, and say plainly what you are not committing to yet. A date with a scheduled re-estimate is worth more to the asker than a confident wrong one, and you should say why in those words.
Follow-up
- The concurrent build fails half way through the largest partition. What is the state of the database and what do you do next?
- Your estimate slips by sixty percent. Which assumption broke, and at what point would you have known?
- The person asking needs the date for a customer commitment. Does your answer change?
- 01
Give an example of a project where you had to balance strict security requirements with rapid feature delivery.
- 02
A colleague's pull request enforces a per-tenant quota by selecting the current count and then inserting when it is under the limit. You flag it as a race. They reply that the transaction already runs at repeatable read, so the snapshot makes it safe, and the tests pass. Walk through taking that disagreement to a resolution: what you write in the review, what you demonstrate rather than assert, which fix you propose and why, and what you do if they still disagree after all of it.
- 03
Someone needs a date. usage_event carries an index on (occurred_at) and needs (tenant_id, occurred_at); the largest tenant holds roughly a hundred times the median tenant's rows, the table is partitioned daily with years of retention, and you have never run a migration on a table this large. Give an estimate you would defend: how you decompose the work, the two or three numbers you would go and measure first, the range and confidence you state, and what you commit to when the person asking needs a single date today.
Is this an official Barclays interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Barclays. Rounds and questions reflect what candidates have reported, not a process Barclays has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the technical interview process at Barclays?
The technical difficulty is generally rated as moderate to challenging. Rather than relying purely on hyper-complex algorithmic puzzles, Barclays focuses heavily on practical coding, technical fundamentals, clean code principles, database querying (SQL), and deep familiarity with your resume and project history.
PracHub interview research ↗How much preparation time should I plan for before the interviews?
Candidates typically benefit from two to three weeks of targeted preparation. Focus your effort on reviewing core computer science concepts (Data Structures, OOP design), practicing SQL query formulation, reviewing your primary programming stack, and preparing structured behavioral stories mapped to the RISES framework using the STAR method.
PracHub interview research ↗What differentiates candidates who receive offers from those who do not?
Successful candidates demonstrate a balanced profile. They possess solid core coding skills, write bug-free SQL, explain their project architecture with clarity, and articulate clear, authentic alignment with Barclays' values (RISES). Candidates who neglect behavioral preparation or cannot explain their resume in depth often fall short.
PracHub interview research ↗Does Barclays emphasize behavioral questions as much as technical coding?
Yes. Barclays places a significantly higher weight on behavioral competencies and organizational fit than many standard technology firms. You will face dedicated competency questions in almost every round, evaluating how you work in teams, resolve conflicts, adhere to compliance standards, and drive projects forward.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-24 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-24 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-24
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