A Software Engineer at Petrofac serves as a vital bridge between complex engineering requirements and the digital systems that power global energy infrastructure. You will not just be writing code; you will be developing tools and platforms that optimize energy production, manage heavy equipment data, and streamline project delivery. This role is critical for transforming raw technical data into actionable business intelligence within an EPC (Engineering, Procurement, and Construction) environment.
You can expect to work on high-impact projects that require a blend of software development rigor and an appreciation for physical engineering constraints. Whether you are building internal tools to optimize supply chain logistics or developing interfaces for monitoring rotating equipment, your work directly influences the efficiency and safety of large-scale operations. It is a challenging, intellectually stimulating role that demands both technical precision and the ability to translate complex engineering concepts into scalable, user-focused software solutions.
Petrofac frequently operates in the energy sector, meaning your software solutions will often need to integrate with or support legacy engineering systems, mechanical data, and rigorous safety standards.
Online Assessment
reportedWhat this round decides is narrow: whether you can produce code that runs and is correct on inputs nobody showed you. An elegant solution that does not compile scores below a plain one that does, so write a correct brute force first, say out loud that you know its cost, and improve it with the working version still on screen. What separates strong answers is who finds the broken case. Trace your own code against an empty input, a single element, and duplicate keys before you say you are finished, because being told is far more expensive than noticing.
What to demonstrate
- Whether degenerate inputs get checked without being asked for: an empty collection, one element, every element equal, and the extreme value the input type allows
- Whether the complexity you state matches the code you actually wrote, including a sort or a copy sitting inside a loop
- Whether the finished answer is verified against the worked examples before you call it done, rather than assumed correct because the code reads correctly
How to prepare
- Take five problems you have already solved and, without running anything, write down what each returns for empty input, a single element, and all-duplicates. Then run them and count how many you predicted wrong.
- Drill the brute force as its own skill: on ten problems, write only the obviously-correct slow version and time how long it takes to get it passing. If that is more than a few minutes, that is what to practise, not the optimal version.
- Add a fixed last step before you submit anything, reading only the loop bounds and the initial value of each accumulator, which is where most off-by-one errors live
Technical Interviews
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.
Behavioral Interviews
reportedMany of these questions are about something that went wrong, and the grading sits mostly in the hours after you knew. Who found out first, whether that was you or an alert or a user, how long it took you to say it out loud, and whether the people who needed the news got it while they could still act on it. Engineers under-tell this part because it feels like confessing. The pattern it is looking for is the opposite: the quiet fix, an incident absorbed without telling anyone, after which nothing changed and the same failure is still available.
What to demonstrate
- How the problem was found, and whether that route was one you had built or one that happened to you, since a user reporting it first means your instrumentation did not cover that failure
- Whether time-to-detect and time-to-tell are separate numbers in your account and whether you know both, because a fast fix that nobody heard about until the retro is a different answer from a slow one that was announced immediately
- Whether the resolution left something durable behind, a check that fires or a default that changed, rather than depending on people remembering to be careful
- Whether you can say what the failure cost without either inflating it or waving it away
How to prepare
- Reconstruct one incident you were part of as a timeline with clock times: first bad request, first signal, first person who knew, first message outside the team, mitigation, permanent fix. The gaps between those entries are what gets asked about
- Look up the configuration of the signal that caught it, including its evaluation window and threshold. An alert defined on a five-minute aggregate cannot fire until the condition holds across that window, which puts a floor under time-to-detect that has nothing to do with how severe the failure was. Be able to say what that floor was and whether anyone had chosen it deliberately
- Prepare one story where you escalated early and the severity turned out to be smaller than you thought, including what it cost the people you pulled in. Without it, every answer you give about raising alarms is unfalsifiable
Presentation/Case Study
reportedThe prompt is deliberately underspecified, and the first ten minutes decide most of the outcome. What separates a strong answer is whether requirements arrive as numbers you then use, rather than a ritual of questions asked and abandoned: how many writes a second, how large a record, how stale a read may be, what must survive a machine dying. Weak answers ask what the scale is, hear a number, then draw the diagram they would have drawn anyway. The round is deciding whether you can bound an open problem tightly enough that a team could build it, and say plainly which parts you are not designing.
What to demonstrate
- Whether the requirements you extract actually change the design: the moment a number you asked for rules an option out is the one being watched for, and its absence is noticed
- Which parts you put out of scope and whether you say so out loud, since an unbounded problem worked at uniform depth ends as a diagram with no decisions in it
- Whether you separate what the system must guarantee from what it may lose, in durability and in staleness, before choosing any storage
- Whether your one-sentence restatement of the problem matches what was actually asked, rather than the adjacent problem you already know how to solve
How to prepare
- Take three open prompts and stop after scoping. Write the five numbers you would need and, beside each, the decision that number settles. Any question that settles nothing is one you are asking out of habit; cut it
- Draft the out-of-scope sentence for each prompt and say it aloud: two things you are deliberately not designing and the condition that would bring them back. It costs nothing at minute five and reads as retreat at minute thirty
- Write a one-sentence problem statement for a prompt, then have someone holding the original compare them and flag what you quietly added or dropped
PracHub editorial advice for the preparation topics above.
Ordering events by arrival rather than by event time
Offline handhelds, batch partner feeds and store-and-forward gateways deliver observations out of order as a matter of course, so a pipeline that folds whatever arrived last will flap a delivered shipment back to in transit and recompute on-time performance from the wrong facts. Message-broker ordering guarantees do not rescue this: per-partition ordering only holds within a partition, so unless the producer keys by the entity being tracked, two events for one leg can land on different partitions and be processed concurrently. The defence has two halves that are often confused - deduplicate on a stable key, then fold with a monotonic status lattice ordered by occurred_at - and both are needed, because deduplication alone still lets a stale event win. Note also that occurred_at is device-reported and therefore sometimes wrong, which is why storing received_at and a measured clock offset beside it is what makes event-time logic auditable instead of merely plausible.
Judging delivery windows and cut-offs in UTC
A delivery appointment, a receiving cut-off and a same-day promise are all statements about local wall-clock time at a specific stop, so evaluating them in UTC misclassifies on-time performance for any leg that crosses a zone, and misses cut-offs by an hour on the two days a year a region changes offset. The subtlety that catches experienced engineers is that converting a future local time to a UTC instant at write time bakes in the offset rules as they were known then: when a jurisdiction changes its rules, an appointment stored that way silently moves, which is why a future window is better stored as local wall time plus an IANA zone identifier and resolved at read time, while a past event is correctly stored as an instant. Duration arithmetic needs the same care, because a transit that spans a DST boundary is genuinely 23 or 25 hours long in local terms even though the elapsed instant count is unchanged. Storing only a date with no zone - common on partner feeds - is the version of this that cannot be fixed downstream, so the zone has to be captured at ingestion.
Starting work without saying what you are about to spend time on
State the plan before executing it: the approach, roughly how long it will take, and what you intend to leave hand-waved. That gives the interviewer a chance to redirect you in ten seconds rather than watching you spend fifteen minutes on the wrong sub-problem.
Answering a debugging question with a guess instead of a bisection
Give a procedure that halves the search space at each step: confirm the symptom reproduces, establish the last known-good version, input or timestamp, then bisect over commits, over the data, or over the layers of the request path. A plausible cause with no way to confirm it is the same move whether it happens to be right or wrong, which is why it scores nothing.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find the hottest write keys under a fixed memory budget
One day of writes to inventory_movement is 200 million rows; each carries (item_id, node_id). Distinct pairs reach 60 million. Return the 200 pairs with the most writes so they can be moved behind a single writer before the next peak. First give the exact answer assuming you may hold 60 million counters, then give an answer under a 200 MB budget, and state the error guarantee that second answer carries. Both must be one pass over the day's rows unless you argue for a second.
Approach
- Exact version: one hash pass to build
(item_id, node_id) -> int64, then a single scan of the map maintaining a min-heap of size K = 200, pushing and popping when the incoming count exceeds the heap root. O(n + d log K) with n = 2e8 and d = 6e7, versus O(d log d) if you sort all 60 million entries — a factor of roughly log d / log K, about 3.4x, for no benefit. The heap must be a min-heap: the root is the weakest survivor, so eviction is O(log K). - Cost the map honestly. 60 million entries at a 16-byte key, an int64 count and per-entry overhead is 2-3 GB in most runtimes, which is the whole reason for the second version rather than a theoretical aside.
- Bounded version: Misra-Gries with m counters. Keep at most m keys with counts; on a key already present increment it; on a new key when fewer than m are held, insert at 1; otherwise decrement every held counter and drop those reaching zero. With m = 1,000,000 at roughly 40 bytes per counter, that is about 40 MB, comfortably inside 200 MB alongside the heap.
- State the guarantee as a number, not as 'approximate'. Misra-Gries returns an estimate satisfying f - n/(m+1) <= f_hat <= f, so with n = 2e8 and m = 1e6 every count is an undercount by at most 200. If the 200th hottest pair has hundreds of thousands of writes, a 200-count error cannot reorder the top of the list — and if the counts near rank 200 are within 200 of each other, the ranking there was never meaningful for the decision being made. The sketch also merges: run one per shard, combine, prune back to m, and the bound holds with n the combined length.
- If exactness is genuinely required, argue for two passes rather than more memory: the Misra-Gries pass yields a candidate superset of size m, and a second pass counts only those candidates exactly in O(m) memory. That is the standard way to get an exact top-K under a memory bound, and the cost is one extra read of the day's rows.
Worked solution 25 min
- Implement the exact path first with a hash map and a size-200 min-heap, and record its peak resident memory on a scaled-down input.
- Implement Misra-Gries with an explicit decrement step, and assert after every insert that the held-counter count never exceeds m.
- Generate a synthetic stream with a known Zipf-ish head: 200 planted heavy keys plus a long tail, so the true top-K is known.
- Run both, diff the two top-200 lists, and measure the largest per-key undercount against the n/(m+1) bound.
- Shard the stream into eight parts, run eight sketches, merge and prune, and confirm the merged result still respects the bound.
Follow-up
- The decision downstream is which keys get a single writer. Does a rank error at position 190 change that decision, and what does the answer tell you about how much accuracy to buy?
- Write volume on one key is bursty — a promotion concentrates it into twenty minutes. Does a daily top-K find it, and what window would?
- How do you keep the sketch across a worker restart without replaying the day?
Split an order across the fewest nodes that can fill it
A fulfilment order has L lines, L up to 25. After filtering by capability, service time and stock, N candidate nodes remain; N is usually under 14 but can reach 200. Each node covers a known subset of the lines in full. Return a minimum-size set of nodes covering every line, tie-broken by total distance. Give an exact algorithm for the small case with its exact cost in operations and bytes, say precisely why enumerating node subsets stops working, and give what you ship when N is 200.
Approach
- Name it: this is minimum-cardinality set cover, which is NP-hard, so the useful move is choosing which parameter you are exponential in rather than hunting for a polynomial exact algorithm. Enumerating subsets of nodes is 2^N and correct — at N = 14 that is 16,384 subsets and finishes instantly, which is exactly why it survives review and then fails in production at N = 200, where 2^200 is about 1.6e60.
- Be exponential in L instead, because L is the parameter you bound. Index the DP by the set of lines still uncovered:
dp[rem]is the cheapest way to cover the lines inrem, withdp[0] = (0, 0)and the value a(node_count, total_distance)pair compared lexicographically. Letbbe the lowest set bit ofrem; any cover must contain some node that stocks lineb, so the transition ranges only over those nodes:dp[rem] = min over v with b in cover(v) of dp[rem & ~cover(v)] + (1, dist(v)). Worst case O(2^L * N) — at L = 25 and N = 14 that is 33,554,432 states times 14, about 4.7e8 transitions — and the lowest-bit restriction cuts the real branching factor to the nodes stocking one specific line. - Quote the memory for what you actually store, not for a count array alone. Over 2^25 masks you need three dense arrays:
uint8node count (33.5 MB),uint32total distance (134.2 MB, integer metres so the tie-break compares exactly and the array stays fixed-width), anduint8chosen node (33.5 MB, since N <= 200 fits a byte) — about 201 MB, roughly six times the 33.5 MB that a bare count array would suggest. The same layout at L = 20 is 6.3 MB, which is why this is comfortable up to about 20 lines and needs a deliberate decision at 25. - Reconstruction is free in this formulation and needs no parent-mask array: because every transition eliminates the lowest uncovered line, the predecessor of
remunder the stored choicevis exactlyrem & ~cover(v), so walkremfrom the full mask down to 0 emittingchoice[rem]. If the reachable state count is far below 2^L — coverage sets are usually highly correlated — a top-down memoized recursion over a hash map trades the 201 MB for roughly 40-60 bytes per visited state; measure the visited count first, because a hash map that ends up touching most masks is strictly worse than the dense arrays. Add a branch-and-bound cut ofceil(popcount(rem) / max_v popcount(cover(v) & rem)), which prunes long before the theoretical bound bites. - When exact is out of reach, ship greedy with a stated guarantee: repeatedly take the node covering the most currently-uncovered lines, using bitset popcounts, O(N * ceil(L/64)) word operations per pick and at most min(N, L) picks. Greedy is within H(L) <= ln L + 1 of optimal (Chvatal), and no polynomial algorithm achieves (1 - o(1)) ln L unless P = NP (Dinur and Steurer, 2014) — so effort belongs in the pre-filter and the tie-break, not in a cleverer heuristic. At L = 25 the bound is H(25), about 3.82x worst case, while the realistic gap on correlated coverage sets is zero or one node.
- Close on the objective, because fewest nodes is not cheapest. Two shipments from distant nodes routinely cost more than three from near ones, and a split also costs a second box, a second carrier pickup and a worse customer experience. If cost is the real objective it is weighted set cover — greedy by cost divided by newly-covered lines, same logarithmic bound — and if a node can cover a line only partially, the line mask is no longer sufficient state, since you must track remaining quantity per line; that is the point at which you stop hand-rolling and either restrict splits or hand an MILP to a solver with a time budget.
Follow-up
- The pre-filter is what keeps N small. What is in it, and what happens to your exact path the day someone loosens it?
- One node can cover a line only partially. Show where the bitmask formulation breaks and what state replaces it.
- You have 40 milliseconds inside a checkout call. Which of these runs there, and what runs asynchronously afterwards?
Parse partner quantity and date segments without floating point
A partner sends a delimited flat file whose segments look like QTY+17:4.500:CS and DTM+232:20260317. Quantities carry up to three decimal places in a named unit; each item has an integer pack factor to eaches that is versioned by effective date. Produce observation_event rows with an integer quantity in eaches, the unit recorded, and occurred_at resolved, rejecting any line whose conversion is not exact. Files reach 4 GB. Single pass, no floating point. State your bounds and what you do with the zoneless date.
Approach
- Scan with indices, not a regex. Find the segment terminator, then split the payload on
:by scanning forward and slicing — O(bytes) with no backtracking and no per-line allocation beyond the output row. A regex with nested quantifiers over 4 GB is where this job silently becomes CPU-bound. - Parse the decimal as an integer mantissa plus a scale.
4.500becomes(mantissa = 4500, scale = 3): accumulate digits into an int64, count digits after the point, and never call the runtime's float parser. Reject a mantissa that would overflow int64 when multiplied by the pack factor, checking before the multiply rather than after. - Convert by integer arithmetic and demand exactness:
numerator = mantissa * factor; emitnumerator / 10^scaleonly whennumerator % 10^scale == 0, otherwise reject the line with a reason code. Worked:4.500 CSat 12 eaches per case gives 54000/1000 = 54, exact.0.125 PLat 36 eaches per pallet gives 4500/1000 = 4.5, which is half a sealed unit and must fail at the boundary, not round three services away. - Select the pack factor by the file's effective date, not by the current item master. Factors are properties of a point in time — a supplier moving a case from 12 to 10 must not reinterpret last quarter's file — so the lookup is a range query on the factor's validity interval.
20260317carries no zone and no time. Attach the IANA zone of the node the segment refers to, captured at ingestion, because it cannot be recovered downstream; then pick and document one convention for a date-only field (start of local day is the usual choice) and store the raw local string beside the resolved instant so the assumption stays visible. Complexity: O(total bytes) time, O(longest line) working space, one pass.
Follow-up
- The same file is re-sent in full tomorrow with three lines changed. What makes the second ingest a no-op for the unchanged lines?
- A line names a unit that is not in the ladder for that item. Reject the line, reject the file, or quarantine it — and who finds out?
- The partner starts sending an offset like
+0100on some dates. Does that change your storage decision for a future appointment as well as a past event?
Stop two allocators promising the same unit of stock
inventory_position is keyed (item_id, node_id, lot_id, state) and holds qty BIGINT, which is on-hand stock and is moved only by the movement ledger; available_qty BIGINT, maintained on the same row; and version. The declared invariant is available_qty = qty - SUM(allocation.qty) over that key in status held or committed, enforced by CHECK (available_qty >= 0) and CHECK (available_qty <= qty). allocation holds allocation_id, order_line_id, item_id, node_id, lot_id, qty, tier, status (held, committed, consumed, released, expired) and expires_at. Twelve allocator threads try to claim the last eight units of one item at one node inside the same 50 ms. Give the exact statements that let at most eight units be claimed, then say what changes if identical code runs under PostgreSQL REPEATABLE READ versus InnoDB REPEATABLE READ, and what a retried allocate for the same order line must do.
Approach
- Collapse the check and the write into one statement whose WHERE clause is the business rule: UPDATE inventory_position SET available_qty = available_qty - :q, version = version + 1 WHERE item_id = :i AND node_id = :n AND lot_id = :l AND state = 'on_hand' AND available_qty >= :q. qty is deliberately untouched: a hold is a promise, not a physical move, so only a movement may change on-hand and the ledger-equals-position invariant survives. Rowcount zero is a refusal, not an error, and the allocation row is inserted in the same transaction only when the rowcount is one.
- If the deployment carries no available_qty column and availability must be derived, the alternative is SELECT ... FOR UPDATE on the position row followed by the SUM over held and committed allocations inside the same transaction. That is correct but holds the row lock for two statements instead of one, which matters on a key taking hundreds of writes a second.
- Isolation is not a substitute for the atomic step, and it changes the error contract. Under PostgreSQL READ COMMITTED the losing UPDATE blocks, then re-evaluates its predicate against the newly committed row version and returns rowcount 0, which the handler already handles. Under PostgreSQL REPEATABLE READ the same statement instead raises SQLSTATE 40001 and the whole transaction must be retried, so a handler that only tests rowcount fails closed on an exception it never expected. InnoDB REPEATABLE READ does neither: a locking read or UPDATE performs a current read of the latest committed row, so the loser blocks and then sees rowcount 0, with a deadlock rather than a serialization failure as its exceptional case.
- Make the retry a no-op with a partial unique index: UNIQUE (order_line_id, node_id, lot_id) WHERE status IN ('held','committed'). Then a retried allocate conflicts instead of double-claiming, and the handler reads the existing allocation and returns it.
- Never source the availability number from a replica or a cache for this path. Replication lag is largest during the write burst that made the key hot, so the guarantee fails precisely when it is load-bearing.
- For the hottest item-node pairs, shed load without overselling: route allocations for that key through a single writer with a bounded queue and refuse with a conservative not-available when the queue is full. Splitting the position into N sub-rows cuts contention N-fold but strands the remainder across sub-rows and produces false out-of-stock when the last units are spread thin.
Worked solution 30 min
- Write the conditional UPDATE, the rowcount branch and the allocation INSERT, plus the partial unique index DDL and the two CHECK constraints the invariant needs.
- Set a position to qty = 8 and available_qty = 8, launch 12 threads each claiming 1, and assert exactly 8 succeed, available_qty lands at 0 and qty is still 8.
- Re-run the same race under PostgreSQL REPEATABLE READ and record how many transactions fail with 40001 rather than returning rowcount 0.
- Send one allocate twice for the same order line and node, and assert one allocation row and the same allocation_id returned both times.
- Remove the available_qty >= :q predicate and confirm CHECK (available_qty >= 0) aborts the losing transactions, proving the test exercises the race rather than serialising by luck.
Follow-up
- One order line needs two units from two different nodes, atomically. What do you do when those positions live in different shards and no distributed transaction is available?
- Soft-tier allocations expire. Show the sweeper statement, and say what stops it from releasing an allocation that is being committed at that instant.
- Under sustained contention your CAS retries. What is the retry budget, and what does the caller see when it is exhausted?
Add a required unit column and uniqueness to a live ledger
inventory_movement holds 4.2 billion rows on PostgreSQL 15 and takes about 2,000 inserts per second around the clock. Two changes are required with no downtime: add uom_code TEXT NOT NULL, whose truthful value must come from the conversion factors in force at each row's occurred_at rather than from today's item master; and make idempotency_key UNIQUE, although duplicates predating the retry fix already exist. Give the ordered steps, the lock each one takes, its expected duration and its rollback.
Approach
- ADD COLUMN with a non-volatile DEFAULT has been metadata-only since PostgreSQL 11, so no rewrite, but it still takes ACCESS EXCLUSIVE for an instant. That lock queues behind any long-running reader while blocking every statement behind it, so one 10-minute analytics scan turns a millisecond change into a 10-minute outage. Set lock_timeout to 2s and retry in a loop rather than waiting.
- Reach NOT NULL without a scan under an exclusive lock: ADD CONSTRAINT ... CHECK (uom_code IS NOT NULL) NOT VALID, which is instant, then VALIDATE CONSTRAINT, which takes only SHARE UPDATE EXCLUSIVE and runs concurrently with inserts, then SET NOT NULL, which PostgreSQL 12 and later proves from the validated check and so skips its own scan. Drop the redundant check afterwards.
- The truthful backfill is the long pole and must read the versioned conversion factor as of each row's occurred_at, not the current item master, or a supplier moving a case from 12 units to 10 retroactively reinterprets last quarter. Batch by primary-key range, a few thousand rows per transaction, with a sleep between batches and a resumable cursor in a control table. At 5,000 rows a second, 4.2 billion rows is roughly ten days of throttled work: that is a schedule, not a failure, and it must not block the deploy.
- The UNIQUE index cannot be built until the duplicates are gone. CREATE UNIQUE INDEX CONCURRENTLY aborts on the first violation and leaves an INVALID index behind that is ignored by queries but still carries update overhead on every insert, and it can only be removed with DROP INDEX CONCURRENTLY. So: find the duplicates, resolve each one as a compensating reversal rather than a DELETE, since every duplicate key is a double-applied movement and deleting it fixes the constraint while leaving the position wrong.
- Then CREATE UNIQUE INDEX CONCURRENTLY, which makes two passes over the table and waits out concurrent transactions, so budget hours here, and note it cannot run inside a transaction block: a migration framework that wraps every step in BEGIN will fail on this one. Promote it with ALTER TABLE ... ADD CONSTRAINT ... UNIQUE USING INDEX, which is a brief ACCESS EXCLUSIVE.
- Question whether the constraint should cover all history. At roughly 40 bytes per key plus overhead, a btree over 4.2 billion rows is in the hundreds of gigabytes, while a retry never arrives a year later. A partial unique index bounded by a fixed occurred_at literal, rotated forward on a schedule, gives the same protection for a fraction of the size, and the predicate must be a literal because now() is not immutable.
Follow-up
- Sequence the application deploy against these steps. When exactly may the code start writing uom_code, and when may it start requiring it?
- The backfill is on day six and the conversion-factor table is itself corrected. What happens to the rows already written?
- How do you roll back after SET NOT NULL is in place and half the traffic depends on the column?
Explain the architecture of the TCP/IP protocol.
Explain the architecture of the TCP/IP protocol.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How would you confirm site facilities without physically visiting the …
How would you confirm site facilities without physically visiting the site?
Approach
- Clarify what is being asked and what a complete answer contains.
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Allocate through a regional failover with unknown replication state
The inventory primary sits in one region with an asynchronous standby in another, typically 2 seconds behind. The region becomes unreachable at peak, with 3,000 allocations and 3,000 ATP reads per second in flight. Decide whether to promote, what happens to allocations that may have committed without replicating, and what ATP answers meanwhile. Then quantify the cost of making the allocation write synchronous instead, given today's 4 ms transaction and about 42 allocations per second on the hottest item-node pair.
Approach
- Split the decision by operation class before touching the failover mechanics. ATP reads can move to the standby at once with a stated staleness, because a 2-second-old read only mis-advertises and the allocation write re-checks anyway. Allocations cannot: promoting an asynchronous standby discards up to 2 seconds of committed allocations, several thousand at this rate, and each one is a promise already made to a customer.
- Recover those from the requester's durable intent, not from the database that just vanished. If the orchestrator wrote its saga step and an outbox row in one local transaction before calling, every lost allocation can be re-driven after promotion under its original idempotency key, and allocation's partial unique index makes a re-drive that did replicate fail closed instead of double-claiming. Without that intent record the loss is simply unrecoverable, and the honest design refuses allocations until the primary returns rather than pretending otherwise.
- Price synchronous replication in the currency that actually binds, which is hot-row throughput rather than average latency. A cross-region synchronous commit adds tens of milliseconds inside the transaction, turning a 4 ms hold into roughly 65 ms and dropping one row from about 250 to about 15 allocations per second, below the 42 demanded - so the promotion tail becomes an outage every promotion. A synchronous standby in a second zone of the same region costs 1-2 ms, leaves the hold near 6 ms and the ceiling near 170 per second, still four times demand.
- So the configuration is: synchronous in-region for zero data loss on a node or zone failure, asynchronous cross-region for the regional case, and promotion of the cross-region standby as a deliberate human decision with a written procedure. Automatic promotion of an asynchronous standby is how a regional blip becomes two primaries and a permanently split ledger that reconciliation cannot repair, because both halves are internally consistent.
- Define the degraded contracts explicitly for the gap. ATP serves the conservative bucketed answer from the standby with staleness stated; allocate returns a typed unavailability so the orchestrator holds lines in sourcing rather than cancelling or retrying them into a storm. After promotion, replay the outbox, then run the reconciliation re-sum before any node's availability is trusted again, because the re-summed positions are the only evidence of what actually survived.
Worked solution 40 min
- Classify every operation as safe or unsafe on a 2-second-stale replica and write that classification down before choosing a failover posture.
- Trace one allocation that committed a second before the outage through promotion, and name the exact durable record that lets you re-drive it.
- Recompute the hot-row ceiling under in-region and cross-region synchronous commit and set both against the 42 per second demand.
- Write the degraded contracts: what ATP returns, what allocate returns, and what the orchestrator does with each.
- Write the post-promotion sequence - outbox replay, reconciliation re-sum, then re-enable - and say what is not trusted until it completes.
Follow-up
- The old primary returns holding writes the standby never saw. What do you do with them, and what does the reconciliation job report about them?
- How do you rehearse this? Describe a game day that exercises the promotion path without risking an oversell in production.
- Which of the two regions' clocks do you trust for occurred_at ordering during the switch, and does it matter here?
Allocation p99 hits nine seconds while database CPU stays flat
At 14:10 allocation p99 went from 30 ms to 9 s and an unrelated read service sharing the connection pool began failing. Database CPU and mean statement time are unchanged. pg_stat_activity shows 80 of 100 connections in state 'idle in transaction' with a mean transaction age of 18 s, and lock waits concentrated on about 30 inventory_position rows. In the same minute a carrier's rate-quote p99 went from 180 ms to 22 s. Give the ordered diagnosis, the immediate mitigation, and the structural fix.
Approach
- Ask whether the database is working or waiting, because the two look the same from the client. Flat CPU and flat mean statement time with most connections in 'idle in transaction' says the database is idle inside open transactions: the application has begun a transaction and gone off to do something else. That state name is the whole diagnosis and it points at application structure, not at the database.
- Tie the holders to the dependency rather than assuming the link. Compare the distribution of now() minus xact_start against the carrier's latency histogram in the same window; when the two track each other, the transaction is wrapping the external call. The 18 s mean transaction age against a 22 s partner p99 is that match.
- Locate the contention: join pg_locks to pg_stat_activity, group by the locked tuple, and confirm the waits concentrate on a few dozen hot (item_id, node_id) keys. Demand concentrates on those rows precisely during a promotion, which is also when partner latency degrades, so the two curves multiply rather than add.
- Mitigate without a deploy where possible. Set a call timeout on the partner shorter than the lock budget, set idle_in_transaction_session_timeout and lock_timeout so a stall sheds instead of queueing, and give the carrier gateway its own connection pool so one degraded partner cannot drain the pool an unrelated service depends on. A circuit breaker on that partner turns a 22 s wait into an immediate, handled failure.
- Fix the structure: take the external call out of the transaction. Write the allocation state change and an outbox row in one short local transaction, let a relay make the rating or tender call asynchronously with a persisted idempotency key, and advance the saga on the result. Lock hold time then equals local work, single-digit milliseconds, and is independent of any partner.
- Accept and handle what the outbox costs you. Publication becomes at-least-once, so the consumer and the external effect must both be idempotent, and the caller now sees an asynchronous outcome it must be designed for. That is the trade: bounded lock hold time in exchange for eventual consistency and duplicate delivery you have to absorb.
Follow-up
- Your mitigation adds a lock_timeout. What does the allocation path do when it fires, and what does the customer see?
- Even with short transactions, 30 rows absorb most writes on promotion day. How do you keep those keys from serialising everything?
- How do you test this before the next promotion without a degraded partner to hand?
Day one measures instead of guessing, under a fixed rubric, and the remaining hours are allocated in proportion to the gaps before any studying begins. The allocation is deliberately not renegotiated midweek, because the area that feels worst on day three is usually the one that is moving.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Diagnostic, scored before you study anything
- Sit a 110-minute diagnostic in four blocks: forty-five minutes on two coding problems, twenty-five on one design prompt taken to interface and data model, twenty of short-answer fundamentals, and twenty delivering two behavioural answers aloud.
- Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct and 0 is stuck, grading the artifact rather than how the attempt felt.
- Allocate days two to five in proportion to 3 minus each block's score, write the allocation down, and commit to leaving it alone.
Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Largest gap: find the boundary rather than the subject
- Split the weakest area into named sub-skills and rate each separately. For coding those are restating the problem, choosing the structure, stating the invariant, turning the invariant into loop bounds, handling empty and single-element input, and accounting for complexity out loud.
- Attempt three items positioned just above where the rating drops off, and for each write the first move you failed to make.
- Re-attempt one of them from blank four hours later with nothing open.
Deliverable: A sub-skill map with the two blocking sub-skills circled.
Practice prompt ↗Practice prompt ↗03Drill the blocking sub-skill by repeating the shape
- Do eight short repetitions of the same shape rather than eight different problems, so what gets practised is the pattern and not the puzzle.
- State the rule you now hold in one sentence, then test it against a case built to break it, a sliding window over an array containing negative values, or a cache-aside read path whose invalidation message is dropped.
- Have someone else read your one-sentence rule and find the precondition you left out.
Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.
Practice prompt ↗Practice prompt ↗04Second gap, plus maintenance on the strongest area
- Run the same sub-skill decomposition on the second-largest gap in half the time.
- Spend twenty-five timed minutes on the block you scored highest, choosing the hardest item you can still finish rather than a warm-up.
- Write whether each area fails you on recall, on setup, or on execution, and set the fix accordingly: repetition for recall, a written checklist for setup, timed work for execution.
Deliverable: A second sub-skill map plus a one-line failure diagnosis for each area.
Practice prompt ↗Practice prompt ↗Worked solution ↗05The gap that is not a skill
- Record one technical and one behavioural answer, then count two things in the playback: seconds before your first clarifying question, and sentences you began without knowing where they would end.
- Practise saying that you do not know, followed by how you would find out, without letting it soften into a guess, and practise stating a complexity or an estimate before being asked for it.
- Redeliver one answer under a hard ninety-second cap, which forces structure ahead of detail.
Deliverable: Two recordings with a counted reduction in time-to-first-question.
Practice prompt ↗Practice prompt ↗06Retest under day-one conditions
- Sit the same 110-minute structure with new prompts of comparable difficulty and score it on the identical rubric.
- For any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
- Write down which single block you would still lose the offer on.
Deliverable: A second scored rubric placed beside the first, with one named remaining risk.
Practice prompt ↗Practice prompt ↗07Full loop under interview conditions
- Run a sixty-minute mock over the two blocks that moved least, with an interviewer briefed to interrupt and change direction mid-answer.
- Write the recovery script for going blank: restate the question, state your assumption, name the first thing you would check.
- Say every rule from the week aloud without reading it, and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive an interruption.
Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Every story you tell gets read for blast radius and judgement: what could have broken, who else it touched, what you knew at the moment you decided. Nobody can audit your code in an hour, so they audit your reasoning instead. Pick work where the call was genuinely yours and the consequences were real enough to remember.
How do you handle working within a multidisciplinary team with diverse…
How do you handle working within a multidisciplinary team with diverse nationalities and backgrounds?
Approach
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on the reasoning.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you manage your time when faced with competing priorities from …
How do you manage your time when faced with competing priorities from different stakeholders?
Approach
- Give the blast radius: what could have broken, and what you measured.
- State the situation in two sentences and spend the rest on the reasoning.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
How do you handle a situation where a colleague is non-cooperative and…
How do you handle a situation where a colleague is non-cooperative and a project deadline is approaching?
Approach
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- What would you do differently if you ran that again?
- What did you decide not to do, and why?
What is your experience with handling rotating equipment or specific i…
What is your experience with handling rotating equipment or specific industrial machinery?
Approach
- Give the blast radius: what could have broken, and what you measured.
- Close with what you would do differently, concretely.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
- 01
How do you handle working within a multidisciplinary team with diverse nationalities and backgrounds?
- 02
How do you manage your time when faced with competing priorities from different stakeholders?
- 03
How do you handle a situation where a colleague is non-cooperative and a project deadline is approaching?
- 04
What is your experience with handling rotating equipment or specific industrial machinery?
Is this an official Petrofac interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Petrofac. Rounds and questions reflect what candidates have reported, not a process Petrofac has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process?
Candidates generally describe the process as average in difficulty. It is rigorous and professional, but it is less about "trick" questions and more about assessing your competency and fit for the team.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates are those who demonstrate not just technical skill, but also a clear, logical way of communicating their thought process. Showing a genuine interest in Petrofac and the energy industry is also a major differentiator.
PracHub interview research ↗How long does the process take?
The timeline can vary significantly depending on the location and the urgency of the role. Some candidates receive offers quickly, while others may go through a multi-stage process that lasts several weeks.
PracHub interview research ↗Is there a specific focus on behavioral questions?
Yes, particularly in rounds involving HR or department heads. You should be prepared to discuss your teamwork, leadership, and how you manage conflict or stress.
PracHub interview research ↗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.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Tesla Software Engineer Interview Guide
Timed practice for Tesla software engineer candidates who lose time to the clock: short problems taken end to end, then reviewed against a written rubric rather than a passing test run.
Waymo Software Engineer Interview Guide
Caching exercises for Waymo software engineer candidates: deciding what staleness a feature tolerates, choosing between expiry and explicit invalidation, and handling the stampede when a hot key expires.
Flexport Software Engineer Interview Guide
Take-home style practice for software engineers preparing for Flexport: build a small service against a fixed time budget, then write down what you left out and why a reviewer should accept that.
Zenix Aerospace Software Engineer Interview Guide
Operational practice for Zenix Aerospace software engineer candidates: picking the signal that catches a bad release early, and telling a latency regression apart from a shift in traffic mix.