A Software Engineer at Seacoast Bank serves as a critical bridge between complex financial systems and modern, secure digital user experiences. You will be responsible for building, maintaining, and integrating the robust applications that power the bank’s operations, ensuring that both internal teams and external customers have seamless access to financial services.
This role is not just about writing code; it is about understanding the intersection of banking regulations, data security, and efficient software architecture. You will likely work on specialized integrations, such as identity governance platforms like Sailpoint, or leverage data-heavy tools like SAS to drive business intelligence. Given the bank’s evolving digital landscape, you will face the challenge of modernizing legacy processes while maintaining the high standards of security and reliability expected in the financial sector.
Initial Screening
reportedThe person on this call usually cannot evaluate your code and does not need to. They write a short paragraph, and that paragraph is what a hiring manager skims when deciding who to put on your loop. So the test is not whether your work was hard, it is whether a non-engineer can repeat it correctly. Name systems by what they did rather than by their internal codename, give each project a shape (what was breaking, what you changed, what happened after), and keep the whole walkthrough near ninety seconds. Depth that cannot survive a paraphrase reads as vagueness.
What to demonstrate
- Whether a non-engineer can restate your projects without distorting them, since their paraphrase is what travels to the hiring manager, not your sentences
- Whether each project has a shape rather than a stack list: the failure or constraint, the change you made, the result and how it was measured
- Whether you can say what was yours inside a team project without either inflating it or disappearing into the plural
How to prepare
- Rewrite each headline project as two sentences with no internal system names and no acronyms outside your company, then say them to someone outside engineering and have them repeat them back. Fix whatever came back wrong
- Attach one measured number to each project: the baseline, the change, and the window it was measured over. Where nothing was ever measured, say that plainly rather than reaching for a plausible percentage
- Time the background walkthrough against a clock. If it runs past two minutes, compress the earliest role to a single clause and spend the recovered time on the most recent one
Technical Discussions
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.
Panel Interview
reportedCoding rounds mostly set a floor. They decide whether you clear the bar, not where you land on the ladder. Level tends to come out of the design discussion and the ownership stories, so the question worth auditing beforehand is whether the scope you describe matches the scope of the job. Work that stops at your own service, or a story whose hard part was writing the code rather than getting several people to agree on an interface, reads a level below where you think you are interviewing, and that gap is usually resolved downwards.
What to demonstrate
- Whether the largest thing you describe owning ran end to end — the decision, the migration path, the rollout, and what you did when it went wrong — or stopped at the change you merged
- Whether design answers include what you would not build, what you would defer, and what you would measure before committing, rather than only what the boxes are
- Whether a disagreement in a story was settled with something checkable — a benchmark, a prototype, a written proposal — instead of by seniority or by waiting it out
- Whether you can say which calls you made alone and which you escalated, and why the line sat where it did
How to prepare
- Write your largest piece of owned work as a timeline of decisions — who decided what, when, and what you did when the plan broke — then delete every sentence whose subject is "we" and see how much survives
- Take one system you know well and drill the migration answer: how old and new paths run side by side under live traffic, how you compare their outputs, what the rollback is once writes are going to both, and which step you would not automate
- Map each line of the ladder in the job posting to a specific thing you have done, find the line you cannot support, and prepare the closest evidence you have plus an honest account of the gap
PracHub editorial advice for the preparation topics above.
Treating money as a decimal with two places
ISO 4217 exponents are 0 for currencies such as JPY and KRW, 2 for most, and 3 for BHD, KWD, JOD, OMR and TND, so a hard-coded multiply-by-100 is off by a factor of 100 or 10 depending on the currency, in opposite directions. Floating point is worse: IEEE 754 binary64 cannot represent 0.1 exactly, so repeated accrual accumulates drift that appears as a handful of minor units in the daily reconciliation and then gets 'fixed' by widening the match tolerance, which is how a genuine break becomes invisible. The only forms that survive a reconciliation are integer minor units with the exponent carried alongside the currency code, or a fixed-scale decimal type with exactly one documented rounding point.
Assuming the default isolation level enforces the invariant you wrote down
PostgreSQL defaults to READ COMMITTED, where every statement takes a fresh snapshot, so a read-modify-write on a balance loses updates under concurrency. Its REPEATABLE READ is snapshot isolation, which blocks that particular anomaly by aborting the loser with SQLSTATE 40001 but still permits write skew across two different rows; only SERIALIZABLE closes that, and both levels therefore require a bounded retry loop on 40001 that many implementations simply never write. MySQL's InnoDB REPEATABLE READ behaves differently again — it does not abort on a conflicting write, so the identical application code silently changes behaviour when the engine changes. Two-sided transfers add a second failure mode on top: without a deterministic lock ordering, such as always locking account ids in ascending order, concurrent opposing transfers deadlock (SQLSTATE 40P01).
Hardcoding to the sample inputs
Solve the stated problem rather than the two examples; special-casing a literal to make a sample pass is obvious immediately and reads as either a misunderstanding or an attempt to fake progress. If you genuinely cannot generalise yet, say which part is a stub and what would replace it.
Sorting when the problem never required a total order
Match the algorithm to the guarantee actually needed: the top k comes from a size-k heap in O(n log k) time and O(k) space, distinctness needs a set rather than an ordering, and a small bounded integer key range admits a linear counting pass. A full O(n log n) sort is the right default only when you genuinely need everything in order.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Match a settlement file to ledger postings under duplicate keys
You have one business date of ledger postings (about 12 million rows: transaction_id, source_id, amount_minor, currency, business_date) and the processor's settlement file (about 12 million lines: settlement_line_id, external_reference, amount_minor, currency, business_date), where source_id carries the external reference. Match one-to-one on (external_reference, amount_minor, currency, business_date). Duplicate keys occur legitimately — the same amount can appear twice. Emit every unmatched item classified ledger_only, file_only or duplicate_match, in linear expected time. Then say what you do when neither side fits in memory.
Approach
- Build the smaller side into
key -> deque of row ids, neverkey -> row id. A duplicate key is data, not corruption; a single-row map drops one of a legitimate pair and the break report then shows afile_onlythat does not exist. - Probe the larger side once, carrying one extra bit per bucket: whether that bucket was ever hit. Pop from the bucket on a match and set the bit. An absent key is a probe-side-only row. A present-but-empty bucket means the probe side holds more copies than the build side — surplus, so
duplicate_match. After the pass, a leftover non-empty bucket that was never hit is build-side-only; one that was hit is build-side surplus, so alsoduplicate_match. - That bit is what makes the classification a function of the per-key counts rather than of which side you happened to build. For a key with L ledger and F file copies: min(L, F) match, and the |L - F| surplus rows are
duplicate_matchtagged with the side that is over, degenerating toledger_onlyorfile_onlyexactly when min(L, F) is 0. Without the bit, surplus is only observable as a present-but-empty bucket, which can only ever happen on the probe side — so the same input reports different break classes depending on build order, and the smaller-side heuristic in bullet one silently decides which. - A genuine amount difference does not surface as
amount_mismatchhere, because the amount is inside the key — it surfaces as aledger_onlyand afile_onlysharing a reference. Promote those in a second, separate pass keyed on reference alone, recording signeddelta_minoras ledger minus file. Keep that promotion out of the exact pass. - Cost: O(N+M) expected time and O(min(N,M)) memory; the hit bit packs into the bucket header and changes neither bound. The constant is the hash map, roughly 60 to 100 bytes per entry in most runtimes, so 12 million rows is order 1 GB — measure it rather than assert it.
- When neither side fits, use a grace hash join: partition both sides with the same hash function into P spill files so a key lands in the same partition on both sides, then join partition by partition in memory. Cost is two extra sequential passes; skew inside one partition is the failure mode, handled by re-partitioning that partition under a second hash.
- Sort-merge is the alternative at O(N log N + M log M) with external sort, and it wins when the file already arrives sorted by reference or the output must be ordered. It also gets the surplus classification for free, since a merge sees L and F side by side. Whichever you pick, do not widen the amount comparison to make breaks disappear: a tolerance wide enough to absorb rounding is wide enough to absorb a real loss.
Follow-up
- The file nets three fee lines into one batch total. Which pass catches that, and what is its stopping rule?
- The processor's business date sits one cutoff behind yours for forty minutes of traffic. What does that do to the exact join, and what does it do to break ages?
- The same break recurs on the next run. Why must it link to the existing
reconciliation_breakrow rather than open a second one?
Compute peak held exposure from overlapping authorisation holds
An account has up to 2 million authorisations on one business date: (auth_id, amount_minor, created_at, expires_at) with the hold live over the half-open interval [created_at, expires_at), plus capture events (auth_id, captured_minor, captured_at) that reduce the hold at captured_at, and explicit reversals that drop the remainder to zero. Timestamps are microsecond-precision timestamptz. Return the maximum total held amount across the day and the earliest instant it is reached, with complexity. Then say what changes if the deliverable is the peak per minute instead.
Approach
- Expand each authorisation into signed delta events rather than reasoning about intervals:
+amountatcreated_at,-remainingatexpires_at,-captured_minorat eachcaptured_at,-remainingatreversed_at. The problem collapses to a running sum over a sorted event list. - Sort the 2n to 4n events by
(timestamp, sign)with negative deltas ordered first on a tie. The half-open convention forces that: at exactlyexpires_atthe hold is already gone, so a-must land before a+at the same instant or you report a one-microsecond peak that never existed. O(n log n) time, O(n) space. - Sweep once, tracking
running,bestandbest_at, taking the first instant that attains the maximum. Say out loud which tie rule you are using — 'the peak' is ambiguous when the same level is reached twice, and the caller needs to know which instant they are being handed. - If timestamps are bucketed (1,440 minute buckets for the per-minute variant), drop the sort for a difference array: add the delta at the start bucket, subtract at the end bucket, prefix-sum once. O(n + B) time and O(B) space, strictly better, at the cost of answering only at bucket resolution.
- Assert the invariant during the sweep:
runningmust never go negative. A negative total means a capture exceeded its authorisation, which is an invariant violation upstream rather than a sweep bug — fail loudly instead of clamping at zero and reporting a plausible number. - Handle carry-in: a hold created before the window contributes its remaining amount as the sweep's initial value, not as a
+event inside the window. Omitting that is the off-by-a-day that makes the first minute of every day look artificially quiet.
Follow-up
- An incremental authorisation raises an existing hold after the fact. Where does that event go, and does it disturb the tie rule?
- You now need the peak for 10 million accounts inside a nightly window. What changes, and what must the partitioning key be?
- The peak sizes a funding transfer. Does the business date or the timestamp decide which day that transfer lands on?
Answer as-of balance queries over an append-only entry log
Given 400 million ledger_entry rows (entry_id, account_id, direction, amount_minor, currency, business_date) and 2 million queries of (account_id, currency, as_of_date) asking for the balance at the end of that business date, produce every answer. The obvious solution — per query, sum that account's entries with business_date <= as_of_date — is correct. Say precisely why it will not finish, then give one that will, with time and space complexity. Corrections are posted as new entries carrying their own business_date.
Approach
- Cost the naive version in numbers before rejecting it. Spread uniformly over 20 million accounts, each query touches about 20 rows behind a per-account index and 2 million queries is 4e7 row touches — perfectly fine. The problem is skew: one pooled clearing or merchant settlement account holding 3e7 entries, taking 10% of the queries, is 6e12 row touches. Name the skew; 'n is large' is not the reason.
- The structural fact that buys a cheap answer: entries are append-only and never updated, so a prefix sum over an account's entries ordered by
(business_date, entry_id)is stable — nothing behind position i can change. No mutable-balance design offers that, and it is why the storage is worth paying for. - Offline sweep, when all queries are known up front: externally sort entries by
(account_id, currency, business_date, entry_id)and queries by(account_id, currency, as_of_date), then merge-walk both with a running sum, emitting each query's answer as the sweep passes its date. O((n + q) log(n + q)) dominated by the sort, O(1) beyond sort buffers, one sequential pass over each input instead of 2 million random seeks. - Online alternative: materialise end-of-day snapshots — one row per
(account_id, currency, business_date)that had activity, holding the cumulative total. A query becomes one index seek for the latest snapshot at or beforeas_of_date, O(log n) per query, over far fewer rows than n. Use snapshots when queries arrive singly and the sweep when they arrive as a batch. - Corrections are the subtlety: an entry posted today but dated back changes historical answers, so every snapshot for that account from that date forward is stale. Either keep a Fenwick tree over dates per account (O(log D) update and prefix query) or recompute that account's snapshots from the corrected date onward. Then be precise about what reproducibility means — yesterday's statement is reproducible as of a stated snapshot time, not identical forever.
- Bound the resources: int64 sums throughout, no float; 400 million rows at roughly 48 bytes of the columns you actually need is about 19 GB, so the sort is external and its fan-out is chosen from the sort buffer, not from the row count.
Worked solution 40 min
- Compute both costs explicitly: the uniform case at about 4e7 row touches, and the skewed case at about 6e12. Showing that arithmetic is the answer to 'why'.
- Implement the offline sweep on a 10-million-row, 50,000-query fixture, merging on
(account_id, currency, business_date, entry_id). - Implement the naive version as the reference answer and assert both agree on every fixture query.
- Add a correction entry dated 30 days back, re-run, and assert that exactly the queries with
as_of_dateon or after that date move, all by the same signed amount. - Measure rows touched and wall time for each at 10 million rows, then extrapolate to 400 million and state the assumption that makes the extrapolation valid — sequential I/O, no random seeks.
Follow-up
- One account holds 30% of all entries. What does the external sort do with it, and what would you do for that one key instead?
- Queries now arrive online at 500 per second. Which design survives, and what does keeping the other one warm cost?
- A correction lands with a
business_date90 days back. Which snapshots are now wrong, and how does a reader find out?
Add and backfill business_date on a live ledger table
ledger_entry holds 4 billion rows, is append-only, takes 10,000 inserts per second, and every reporting query currently derives the business date as posted_at::date. You must add business_date date NOT NULL, populated from the cutoff rule (17:00 in the account's own timezone), backfilled across all history, indexed, and cut over, with no write downtime and no long-held lock. Give the ordered migration steps with the lock each one takes, how you make the backfill restartable and throttled, and how you retire the old expression safely.
Approach
- Add the column nullable and with no default. ALTER TABLE ... ADD COLUMN takes ACCESS EXCLUSIVE but is a catalogue-only change held for microseconds. The hazard is the lock queue, not the statement: a blocked ALTER waits behind one long reader holding ACCESS SHARE, and every query arriving afterwards queues behind the ALTER's pending ACCESS EXCLUSIVE, so set lock_timeout to a couple of seconds and retry rather than letting a metadata change take the table down.
- Deploy the write path before the backfill, so new inserts populate business_date from the cutoff rule while reads stay on the old expression. The backfill then chases a closed set with a fixed upper bound instead of a moving target.
- Backfill in bounded batches keyed by entry_id range, on the order of 50,000 rows per statement, committing between batches and recording the high-water mark in its own table so a killed run resumes instead of restarting. WHERE business_date IS NULL makes each batch idempotent, and the pacing is set by replica lag and dead-tuple growth rather than CPU, since each UPDATE writes a new row version and the WAL volume is proportional to the rows touched.
- Install the constraint without a blocking scan: ALTER TABLE ... ADD CONSTRAINT ck_business_date CHECK (business_date IS NOT NULL) NOT VALID takes a brief ACCESS EXCLUSIVE and scans nothing, then VALIDATE CONSTRAINT takes SHARE UPDATE EXCLUSIVE and runs alongside reads and writes. On PostgreSQL 12 and later, SET NOT NULL can then use the validated CHECK and skip its own full scan; on 11 and earlier it always scans, so the CHECK is the migration on those versions.
- Build the index with CREATE INDEX CONCURRENTLY, which avoids ACCESS EXCLUSIVE at the cost of two table passes, cannot run inside a transaction block, and on failure leaves an INVALID index that must be dropped and rebuilt rather than reused.
- Cut over behind a flag: run the new and old expressions side by side for one reporting cycle and compare totals per day, since the cutoff rule will legitimately move entries near 17:00 across the boundary. Only once they reconcile do you retire the posted_at::date expression index, and you keep posted_at as the ordering key rather than repurposing it.
Worked solution 40 min
- Write the migration as numbered SQL statements, annotating each with its lock mode and expected duration, and set lock_timeout plus a retry around every ALTER.
- Rehearse on a 10-million-row copy under a concurrent insert load, measuring per-batch duration, WAL generated and replica lag.
- Kill the backfill at a random point, restart it, and confirm it resumes from the high-water mark.
- Add the CHECK as NOT VALID, VALIDATE it while inserts continue, then SET NOT NULL and build the index CONCURRENTLY.
- Run old and new date expressions side by side for one cycle and diff the daily totals before dropping the old expression index.
Follow-up
- Reporting now wants ledger_entry partitioned by business_date. Why can this not be another ALTER, and what is the migration instead?
- Nightly totals move for the days around the cutoff change. How do you tell a correct restatement from a backfill bug?
- The backfill is halfway done when a replica falls 20 minutes behind. What do you throttle, and what do you refuse to throttle?
Decide whether balance is derived or materialised, then hold the floor
Authorisation needs an account's available balance inside an 80 ms budget at 3,000 requests per second; that account already has 200 million ledger_entry rows. A product rule says the balance may never fall below the account's negative overdraft limit. Decide whether the balance is summed from entries or held in a materialised account_balance(account_id, balance_minor, floor_minor, currency, version) row, and justify the choice from the read pattern. Then give the write path that holds the floor, naming the isolation level, the anomaly a weaker level permits, and the SQLSTATE you retry.
Approach
- Size the derived read before arguing about it: summing 200 million rows is not an 80 ms operation under any index, since even a covering index on (account_id, entry_id) still reads work proportional to the rows. The authorisation read pattern forces one materialised row fetched by primary key; the statement read pattern, which is low-rate and historical, stays derived from entries. That is the whole justification for the denormalisation, and its price is a write on that row per posting.
- Put the floor where it can be a single-row constraint: CHECK (balance_minor >= floor_minor) on account_balance, updated in the same transaction as the entries. As a predicate over a set of entry rows it cannot be a CHECK at all, which is the reason the materialised row earns its keep twice.
- Write the mutation as one statement: UPDATE account_balance SET balance_minor = balance_minor - $1, version = version + 1 WHERE account_id = $2 AND balance_minor - $1 >= floor_minor. Zero rows updated means refused. This is safe even under READ COMMITTED, because the UPDATE re-evaluates its predicate against the locked, post-update version of the row.
- Name the anomaly in the shape that is not safe: SELECT the balance, compute a new value in application code, then UPDATE to that constant. Every statement under READ COMMITTED takes a fresh snapshot, so two concurrent 60-unit withdrawals against 100 both read 100 and both write 40. The row then claims 40 while 120 has actually left, so the true position is -20, below a floor of 0, and the row it is checked against cannot show it. PostgreSQL REPEATABLE READ is snapshot isolation and aborts the loser with SQLSTATE 40001; SERIALIZABLE additionally closes write skew across two rows. Both require a bounded retry with backoff, and InnoDB REPEATABLE READ does not abort at all, so identical code changes behaviour on a different engine.
- For a two-account transfer, acquire the rows in a deterministic order such as ascending account_id; without it, opposing concurrent transfers deadlock and the database kills one with SQLSTATE 40P01. That is a retry, not a correctness failure, but it is a retry somebody has to write.
- Finish on the ceiling: the hot row admits one committed write per lock hold, so at a 2 ms hold it caps near 500 per second regardless of cores. Sharding into N sub-rows multiplies throughput and immediately makes the floor check cross-row again, which then needs the shard sum under SERIALIZABLE or a per-shard reserved allowance.
Follow-up
- Shard the balance into eight sub-rows. Write exactly what the floor check now does, and what it costs per authorisation.
- Someone proposes an AFTER INSERT trigger on ledger_entry to maintain the balance. What does that change about ordering, about batch posting, and about failure handling?
- How do you detect that the materialised row has drifted from the entries, how often do you run it, and on which replica?
Can you explain your familiarity with SAS or other statistical concept…
Can you explain your familiarity with SAS or other statistical concepts in a production environment?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer contains.
- 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 secure an application from unauthorized outside access?
How would you secure an application from unauthorized outside access?
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Design the error taxonomy for a money-moving API
The caller is a merchant's server-side integration behind a 2 s timeout; on any non-2xx it consults a generic retry policy. Design the error contract for POST /payments: the response media type, the fields every error carries, and the classification a caller switches on. Cover input validation, authentication, state conflicts, rate limiting, indeterminate upstream outcomes, and issuer declines. For each class give the status code, whether a retry is safe, and whether that retry reuses the idempotency key. Deliver the schema and the classification table.
Approach
- Fix the envelope first: application/problem+json (RFC 9457, which obsoletes RFC 7807) with type, title, status, detail and instance, plus two extension members, a stable machine code and a retry class. Callers switch on the code; the prose is for humans and must be free to change.
- Separate transport failure from business outcome. An issuer decline is a completed call with a definite answer, so it belongs in the payment representation as 200 with status failed and a decline code. Putting it behind a 4xx or 5xx invites every generic retry layer in the caller's stack to re-present a card the issuer already refused.
- Classify the rest on two axes: is the resulting state known, and is the caller at fault. Known and caller's fault gives 400/422 for validation and 401/403 for credentials, never retryable. Known and ours gives 409 for a fingerprint or version conflict, retryable only after the caller changes something. Unknown gives 502/504 or an explicit status of processing, retryable with the original key. Overload gives 429 with Retry-After.
- Write the rule for the unknown class explicitly: the retry must carry the same idempotency key and the server must replay the stored response, so repeated attempts converge on one effect rather than several.
- Pin the taxonomy with a test that asserts every error path emits a code from the enumerated set. Without it the catch-all handler produces an uncoded 500 and the caller's switch silently falls through to its default branch, which is usually retry.
Worked solution 20 min
- Write the problem+json schema: type, title, status, detail, instance, code (stable enum), retry_class (never | after_backoff | same_key).
- Enumerate ten concrete failures from the real path: missing currency, unknown merchant, expired instrument token, key reused with a different body, concurrent duplicate key, quota exceeded, processor timeout, processor unreachable, issuer decline, insufficient funds.
- Place each one in the table with its status code, code, retry_class and the caller's action in one line.
- Re-read the two decline rows and confirm they are 200 responses carried in the payment representation, not entries in the error envelope.
- Write the caller-side switch in pseudocode and check that every branch is reachable from the table and that nothing falls through to a default that retries.
Follow-up
- Two requests arrive with the same idempotency key while the first is still in flight. What status do you return, and what is the caller supposed to do with it?
- How do you add a new error code later without breaking a client that switches exhaustively over the current set?
- The caller reports that a 504 from you was followed by a successful payment they never saw. What in this contract lets them discover that without charging twice?
Duplicate captures appear only in production, roughly weekly
About once a week one payment is captured twice. The idempotency path is: SELECT id, response_body FROM idempotency_key WHERE scope = $1 AND key = $2; if no row, call the processor; then INSERT. The table has UNIQUE (scope, key). Logs for each duplicate show one successful capture pair and one HTTP 500 carrying SQLSTATE 23505. A 200-iteration sequential test passes, and a 50-thread version passes on a laptop but fails on the production-sized cluster. Explain why, and give the fix.
Approach
- Read the 23505 as evidence, not as noise. A unique violation on the INSERT proves two requests both passed the SELECT and both reached the INSERT, which means both had already called the processor. The duplicate charge happened before the constraint fired. The constraint is reporting the race; it is not causing it, and anyone who treats the 500 as the bug fixes the wrong thing.
- Name the interleaving precisely. Under READ COMMITTED each statement takes a fresh snapshot, so two concurrent requests with the same key can both run the SELECT before either INSERTs and both see zero rows. Raising the isolation level does not fix check-then-act by itself, because at SELECT time the first transaction has written nothing to conflict with; SERIALIZABLE only converts the race into a 40001 abort that the code must then retry.
- Explain the reproduction gap rather than calling the bug rare. The window is the duration of the processor call: milliseconds against a stub on a laptop, hundreds of milliseconds against a real processor. Production retries are also correlated, since a client timeout produces a second request at a predictable delay, while a thread-pool test fires all 50 within microseconds and lands them on the same side of the window. The local test is not exercising the window at all.
- Restructure so the database picks the winner before any side effect. INSERT the key first with ON CONFLICT (scope, key) DO NOTHING RETURNING id. A returned id means this request owns the effect and may call the processor. No returned row means another request owns it, and note that RETURNING yields nothing on conflict, so the loser must then SELECT the existing row explicitly. Mutual exclusion now lives in one atomic statement and the window is gone.
- Give the loser something to read. If the winner is still in_progress, the loser must neither error nor perform the effect: it polls the row inside the caller's timeout budget and replays response_status and response_body once status is completed, or returns 409 when request_fingerprint differs. Without this, deduplication turns a successful payment into a visible failure.
- Close the crash window separately, because the atomic insert does not cover it. If the winner dies after calling the processor and before writing completed, the row stays in_progress with a stale locked_at. Recovery must query the processor for that key or client reference rather than assume either outcome, which is why the processor's own idempotency key has to be the same value, generated once by the caller and reused on every attempt.
Follow-up
- The same key arrives with a different request_fingerprint. What do you return, and why is returning the cached response wrong?
- What is your locked_at staleness threshold, and what does the sweeper do when it finds an expired one?
- Write the test that fails on the laptop. What do you have to inject to make the window observable there?
Four days sample coding, design, fundamentals and the practical rounds at deliberately shallow depth, which is enough to surface the topics you did not know were in scope. That map, rather than a guess made on day one, decides where the last three days go.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Coding, one pass at shallow depth
- Solve one problem from each of six families, an array with two pointers, hash counting, binary search, a tree traversal, a graph traversal and one dynamic program, under a hard twenty-minute cap with no extensions, marking each finished, late, or stalled.
- For every stall, write the exact move you could not make rather than the subject, so the note reads could not turn the recurrence into a loop rather than bad at dynamic programming.
- Fix nothing today. The value of the pass is the unfixed record.
Deliverable: Six timed attempts marked finished, late or stalled, each stall carrying a named blocking move.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Design, one pass at shallow depth
- Spend twenty minutes each on three different shapes, a read-heavy feed, a write-heavy ingest path, and something needing a transaction across two entities, stopping each at requirements, interface and data model.
- After each, write the first question you could not answer, which is usually a number you could not estimate or a failure mode you had no vocabulary for.
- Mark which of the three you would be most relieved not to be asked, and treat that as data rather than as a preference.
Deliverable: Three shallow designs, each with the first unanswerable question written at the bottom.
Practice prompt ↗Practice prompt ↗03Fundamentals and the practical rounds
- Answer eight short questions in writing at four minutes each, covering the material that fills the gaps between the big rounds: what happens between a URL and a rendered page, what an index costs on write, when a process is preferable to a thread, and what conditions a deadlock requires.
- Do one thirty-minute practical task of the kind a take-home compresses: read an unfamiliar two-hundred-line file and write what it does, what you would change, and the one thing you remain unsure of.
- Score every answer fluent, correct but slow, or absent, and keep the absent ones visible.
Deliverable: Eight scored short answers and one written reading of unfamiliar code.
Practice prompt ↗Practice prompt ↗04The rounds that are about you, and the map
- Deliver three behavioural answers aloud against a timer, a conflict, a failure you owned, and a decision made without enough information, marking any that ran past three minutes or contained no number.
- Assemble the map: every marked item from days one to three on a single page, sorted by how likely it is to appear in your loop rather than by how uncomfortable it felt.
- Choose exactly two areas for the remaining three days and write down what you are deliberately abandoning.
Deliverable: A one-page scored map of the whole surface area with two areas chosen and the rest explicitly abandoned.
Practice prompt ↗Practice prompt ↗Worked solution ↗05First chosen area, to the depth you skipped
- Work the higher-ranked area in four focused blocks, choosing items one level above where you stalled rather than repeating what already works.
- After each block write the rule you extracted in one sentence with its precondition attached, since a rule carrying no precondition is exactly what fails under a variation.
- Re-attempt the day-one or day-two item that exposed this area and compare against the original timing.
Deliverable: Four worked blocks, a timed re-attempt against the original, and three one-sentence rules with preconditions.
Practice prompt ↗Practice prompt ↗06Second chosen area, where the gap is coverage rather than speed
- Treat the second area differently from the first. Day five drilled something you could already half-do; this one is usually a topic you had simply never met, so build one worked reference example end to end and keep it, rather than attempting six problems badly.
- Write down the vocabulary you were missing on day two or three, five terms at most, each with the one sentence that makes it usable in an answer rather than the textbook definition.
- Redo the shallow attempt that exposed this area and note whether you now fail later in the problem, because moving the failure point is the realistic gain from a single day and is worth more than a score that did not change.
Deliverable: One worked reference example for the newly covered area, a five-term vocabulary list, and a note on where the failure point moved.
Practice prompt ↗Practice prompt ↗07Reassemble the loop
- Sit two rounds back to back with no gap, ordering them so the area you chose second comes last, because the map was built from rested, isolated attempts and the loop will reach your weaker area when you are already spent.
- Write where the second round suffered from the first, which is normally the point at which structure collapses into narration.
- Reduce the week to one page holding only the rules you can state without reading them.
Deliverable: Mock notes on cross-round carryover plus a one-page card of rules you can recite from memory.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Team size, service count and tickets closed say very little. Seniority shows in the decision you owned: what you chose not to build, which constraint you traded away, whose objection you had to resolve before anything could move. A large project where you executed someone else's plan is a small story.
What is your process for troubleshooting a system failure in a secure …
What is your process for troubleshooting a system failure in a secure environment?
Approach
- Name the disagreement and how you resolved it with evidence.
- Close with what you would do differently, concretely.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle situations where internal requirements for a project…
How do you handle situations where internal requirements for a project seem to shift or conflict?
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
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
Can you describe your experience developing rules in Sailpoint?
Can you describe your experience developing rules in Sailpoint?
Approach
- Name the disagreement and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on the reasoning.
- Close with what you would do differently, concretely.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
Describe a time you had to integrate a complex tool with existing lega…
Describe a time you had to integrate a complex tool with existing legacy applications.
Approach
- Name the disagreement and how you resolved it with evidence.
- Close with what you would do differently, concretely.
- 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?
- 01
What is your process for troubleshooting a system failure in a secure environment?
- 02
How do you handle situations where internal requirements for a project seem to shift or conflict?
- 03
Can you describe your experience developing rules in Sailpoint?
- 04
Describe a time you had to integrate a complex tool with existing legacy applications.
Is this an official Seacoast Bank interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Seacoast Bank. Rounds and questions reflect what candidates have reported, not a process Seacoast Bank has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews?
The difficulty level is generally average, but it can feel high if the interviewers focus heavily on niche product features rather than general engineering principles. Be prepared for both high-level architecture and granular tool-specific questions.
PracHub interview research ↗How long does the process take?
The process typically moves quickly through the interview stages, but be aware that internal priorities at Seacoast Bank can sometimes shift, leading to delays or changes in the role's status.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates are those who demonstrate a deep, practical understanding of the tools mentioned in the job description while maintaining a professional, collaborative attitude during panel interviews.
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
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