As a Software Engineer at Open GI, you are at the heart of delivering mission-critical insurance technology solutions. This role is pivotal in maintaining and evolving the complex platforms that power the insurance market, requiring a balance of robust coding standards and an ability to navigate intricate business logic. Your work directly influences the reliability and performance of systems that thousands of users depend on daily.
You will contribute to a collaborative engineering environment where technical precision meets high-stakes problem-solving. Whether you are working on core backend services or modernizing frontend interfaces, you are expected to be more than just a coder; you are a problem-solver who understands the lifecycle of a software product. The role offers the chance to work on systems that demand high availability, making it an ideal position for engineers who thrive in structured, professional environments where quality and reliability are the primary benchmarks of success.
Open GI values engineers who can demonstrate a clear understanding of the "why" behind their code, not just the "how." Be prepared to articulate your architectural decisions clearly.
Initial Conversation
reportedAn unlabelled round is first an information problem, and the cheapest information is free. Whoever schedules it can usually tell you how long it runs, who will be in the room and what they work on, whether you will be writing code and in what environment, and whether anything is being sent beforehand. Ask in writing so the answer is on record, then prepare for the two or three formats those answers still leave open instead of betting on one. What separates a strong candidate is not guessing right; it is having an opening that works whichever one it turns out to be.
What to demonstrate
- Whether you can start work from an ambiguous brief, since tolerating a vague scope without stalling is the same thing the job asks for
- Whether the questions you asked beforehand were ones that change your preparation, such as duration, medium and who is joining, rather than ones whose answers you could not have acted on
- Whether you adapt when the round turns out to be something other than what you were told, instead of spending the first ten minutes visibly recalibrating
How to prepare
- Send one short scheduling message asking four things: how long, who is joining and what they work on, whether you will be writing code and where, and whether to prepare anything in advance. Treat a vague reply as real information, since it means the round is loosely structured and you will be shaping it yourself.
- Write one opening that works in any of the formats still open: restate in your own words what you have been asked to do, then ask which of two directions is more useful to them. Say it aloud until it stops sounding recited.
- Set up for the two most likely formats before the call starts, with a blank editor in the language you would choose and a shared document you can type into, so a format surprise costs you nothing in the first minutes
Technical Assessment
reportedInput bounds are the part of the prompt most often skimmed, and they usually contain the answer. They tell you which complexity class is admissible, which narrows the search before you have thought about the problem itself. As a rough planning figure, a compiled language does on the order of 10^8 simple operations per second and an interpreted one roughly an order of magnitude less. So n up to about twenty admits enumerating subsets, a few thousand admits a quadratic pass, and a million admits neither: you need near-linear, or linear with a log factor. If the bounds are missing, ask for them.
What to demonstrate
- Whether the approach is justified by the stated input size rather than by whichever pattern you recognised first
- Whether you ask about the properties that change the algorithm: whether the input arrives sorted, whether duplicates occur, whether values are bounded integers, whether it all fits in memory
- Whether you can name the bottleneck in your own solution and what would remove it, even when you deliberately leave it in place
- Whether a claimed speedup is real, since memoising a recursion only helps when subproblems genuinely overlap and the state can be keyed cheaply
How to prepare
- For each algorithm you rely on, write down the largest n it handles in roughly a second, then check two of those figures by timing them in the language you will actually type in
- For two weeks, write one line naming your target complexity and the bound that justifies it before you write any code, then compare that line with what you ended up submitting
- Practise the conversion backwards: given a required O(n log n), list the mechanisms that get you there (sorting, a heap, an ordered map, divide and conquer) and choose by what the problem needs to query, not by what you used last
Final Interview
reportedWhere the day includes a partner from product, design or data, that conversation is weighted like the technical ones and prepared for least. They are deciding one thing: whether having you in the room makes their decisions cheaper. That means options with costs attached, not implementation detail and not "it depends". An estimate someone can plan against — a range, the assumption that would push it to the high end, and what you would drop to hit the low one — is worth more than a confident single number, which everyone present already knows is wrong.
What to demonstrate
- Whether an estimate comes as a range with the assumption most likely to break it, and states what a specific scope cut would actually buy
- Whether a technical constraint is handed over as a choice with consequences on their side, rather than as a verdict they have no standing to argue with
- Whether you establish what decision is on the table before proposing anything
- Whether risk is raised while it can still change the plan, with the trigger that would confirm it, instead of reported afterwards as a slip
How to prepare
- Take a project that shipped late and write the two-sentence warning you could have given three weeks earlier, naming what you would have needed decided at that point
- Rehearse one estimate out loud until it arrives in three parts: the range, the single assumption that would blow it, and the smallest thing you would cut to protect the date
- Rewrite an objection you have actually made — the "we can't do that" version — as two options with their costs, so the choice ends up with the person who owns it
PracHub editorial advice for the preparation topics above.
Checking a quota with a select and then writing
Under read-committed isolation, two concurrent transactions both observe a count below the limit and both insert, so the limit is exceeded by exactly the concurrency. Repeatable read does not rescue it either: it provides a stable snapshot, and this is write skew, which snapshot isolation permits by design. The options are serialisable isolation, which detects the conflict and aborts one transaction with a serialisation failure and therefore obliges the caller to retry; a single statement with the predicate inside the write; or a constraint that makes the surplus insert fail outright. The reason this pattern survives review is that it is correct in every test that runs one request at a time.
Paginating a growing table with limit and offset
Two unrelated defects share the idiom. Correctness: rows inserted or deleted between page requests shift the window, so a consumer walking an export skips rows and sees others twice, which for a customer-facing sync is silent data loss rather than an error anyone notices. Cost: the database still produces and discards the skipped rows, so page N costs time proportional to N times the page size and a deep page on a large table degrades from milliseconds to seconds. Keyset pagination over a stable, unique, indexed ordering -- where (created_at, id) < ($1, $2) order by created_at desc, id desc limit $3 -- is constant-cost per page and immune to shifting, on the precondition that the cursor columns never change value for a row, which disqualifies updated_at as a cursor.
Assuming the bug is in the framework
Suspect your own code first: read the stack trace top to bottom, check which versions are actually installed rather than which ones you believe are, and reproduce in isolation before blaming a library that thousands of people run daily. When the fault really is upstream, you need that minimal reproduction to say so credibly anyway.
Listing technologies instead of trade-offs
Name the property the design needs first, such as ordered range scans, multi-entity transactions, cheap appends, or a predictable p99, then pick something that provides it and say what it gives up in exchange. Almost any component is defensible once you state the requirement it satisfies and the one it sacrifices.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find peak concurrent sandbox usage from run intervals
Given up to 5 million job_run rows for one tenant over one day, with run_id, started_at, finished_at, status and wall_clock_limit_seconds, report the maximum number of sandboxes running at once, the earliest instant that maximum is reached, and the first run_id that would breach a per-tenant cap of C. started_at is null while a run is queued; finished_at is null both for runs still executing and for runs in status lost. Treat a run as occupying [started_at, finished_at). Give the complexity and state how you handle each null.
Approach
- Turn each run into two sweep events,
(started_at, +1)and(end, -1), then sort the 2n events by timestamp with-1ordered before+1at equal timestamps. That tie-break is what makes the interval half-open, so a run finishing at 10:00:00 and one starting at 10:00:00 never overlap. - Decide each null out loud before sweeping, because each choice moves the answer. A null
started_atmeans queued and contributes nothing. A nullfinished_atwith statusrunningorleasedis clipped to the window end. Statuslosthas no observed end at all, so clip it atstarted_at + wall_clock_limit_secondson the grounds that the supervisor owns the timeout, and record that you did. The table'scheck (finished_at is null or started_at is not null)guarantees you never see an end without a start. - Sweep once, maintaining a running counter, the maximum, and the timestamp at which the maximum was first attained (update
peak_atonly on a strict increase, or you will report the last such instant instead of the earliest). Capture the firstrun_idwhose+1takes the counter to C+1 during the same sweep rather than in a second pass. - Complexity: O(n log n) dominated by the sort, O(n) space. If rows already arrive ordered by
started_at, a min-heap of end times gives O(n log k) time and O(k) space with k the peak concurrency, which is the better shape when the rows come from an index scan on(tenant_id, started_at). - If second resolution is acceptable, counting-sort the endpoints into an 86,400-slot delta array and prefix-sum it: O(n + T) time and O(T) space, which beats the comparison sort at 5 million rows. It answers only at second granularity, so state which resolution the cap is defined in.
Follow-up
- Now report peak concurrency per tenant for 10,000 tenants from one globally sorted stream. What changes about memory and about the sort?
- The cap has to be enforced at dispatch rather than reported afterwards. What does the admission check look like, and where does it race?
- How would you answer 'peak concurrency within any 5-minute window' without re-sorting?
Locate a billing reconciliation gap without rescanning ninety million events
A tenant's sealed invoice total is 0.4% below the sum of its raw usage_event rows for the period. That tenant has 90 million events over 30 days in a table partitioned daily on ingested_at, and its rollups carry source_max_ingested_at, revision and sealed_at. Recomputing all 30 days from raw is correct, and you are not going to do it. Give the procedure that locates the divergent (workspace, sku, hour) cell, the cost of each probe, and the one query you run before any of it.
Approach
- Run the free query first. Sum raw quantity for the period restricted to
ingested_at <= source_max_ingested_atof the sealed rollups, and compare that against the unrestricted sum. The rollup stores the watermark precisely so this can be answered without a scan. If the whole 0.4% sits above the watermark, nothing is broken: it is late data, it becomes an adjustment line, and the investigation ends in one query. - Only if the gap survives that test do you bisect, and you bisect by dimension rather than by rows. Compare 30 per-day totals, then inside the offending day compare the 6 SKUs, then the workspaces, then the 24 hours. That is roughly 30 + 6 + W + 24 grouped probes, each an indexed range scan over one daily partition for one tenant, against O(N) per attempt for the naive re-fold.
- Quantify why naive is not merely slow but unusable mid-incident: at a generous 200,000 rows/second sequential, 90 million rows is about 7.5 minutes per attempt, you will want ten attempts, and every one competes for I/O on the same partitions live ingest is writing. The diagnostic worsens the backlog it is diagnosing.
- Before fetching each comparison, state what it would look like under each hypothesis. Two adjacent hours off by equal and opposite amounts is
occurred_atversusingested_atbucketing. A whole day offset by exactly N hours is a timezone applied at the wrong layer. A gap confined to one SKU in one workspace is an environment filter. The same(tenant_id, idempotency_key)present in twoingested_daypartitions is the dedup horizon losing a retry that crossed midnight. - Make the next bisection cheap by storing the aggregate you keep recomputing. A per-
(tenant_id, ingested_day)count and quantity checksum turns step two from thirty probes into one read, and it is the same number the reconciliation job already produces. - Whatever you find, the sealed period does not change value. The correction is an adjustment line pointing at the line it reverses, carrying its own
source_rollup_watermark, because the original invoice is the evidence of what the customer was charged.
Worked solution 35 min
- Reproduce the shape locally: generate 2 million events for one synthetic tenant over 5 days, fold them, then inject three defects, namely 0.2% of events bucketed by
ingested_at, a handful of duplicateidempotency_keyvalues whose retries cross midnight, and a block of events ingested after the seal. - Write the watermark-bounded query first and record how much of the gap it explains on its own.
- Bisect by day, then SKU, then hour, recording the probe count and the rows each probe touches.
- For each located cell, write down the predicted signature before querying it, then check whether the data matches the prediction.
- Time a full re-fold of the 2 million rows and extrapolate to 90 million.
Follow-up
- The gap is 0.4% in one direction on one day and 0.4% the other way the next day. What does that shape rule in, and what does it rule out?
- How do you distinguish a duplicate from a restatement, given
revisionandrecomputed_aton the rollup? - Ingest is still running while you investigate. What makes your two numbers comparable at all?
Schedule ordered webhook retries with a heap of subscription queues
Design the in-memory scheduler for webhook delivery. Up to 20 million rows sit in status pending or failed_retryable across 200,000 subscriptions, each row carrying next_attempt_at and attempt_count, and each endpoint having a circuit breaker. Deliveries for one subscription must be attempted in order, so at most one attempt per subscription may be in flight. Support due(now), complete(delivery, outcome) and insert(delivery) in O(log S), where S is the subscription count rather than the delivery count. Give the backoff formula you schedule retries with.
Approach
- Key the global heap by subscription, not by delivery. Each subscription owns a FIFO of its due deliveries in event order; the heap holds one entry per eligible subscription, keyed by its head's
next_attempt_at. That is 200,000 heap entries instead of 20 million, and it makes the one-in-flight rule structural rather than a check somebody can forget. due(now): peek the minimum. If its key is in the future, sleep until then instead of spinning. Otherwise pop it, move the subscription into an in-flight set, and do not re-push it. A subscription absent from the heap cannot be dispatched twice, which is precisely how ordering is preserved.complete: on success, drop the head and re-push the subscription keyed by its new head, or leave it out when the queue empties. On a retryable failure, incrementattempt_countand setnext_attempt_at = now + uniform(0, min(cap, base * 2^attempt)), sampled uniformly across the whole interval. That is full jitter; deterministic backoff re-synchronises the herd you just created.- Circuit breaker: park the subscription in a second heap keyed by its half-open time, so an endpoint dead for six hours costs one heap entry and zero attempts rather than consuming worker slots. Admit exactly one probe at half-open and close the breaker only on its success.
- Say the price of the ordering guarantee out loud. One in-flight attempt per subscription means an endpoint answering in 10 seconds drains at 0.1 deliveries/second however many workers you run, and its backlog grows until it recovers. If the customer does not need order, allow k in flight and document delivery as unordered; that is the trade, and it is a product decision.
- All three operations are O(log S) with O(S) resident heap memory and the queues themselves backed by the store. The database-backed equivalent is a partial index on
(subscription_id, next_attempt_at) where status in ('pending','failed_retryable')claimed withFOR UPDATE SKIP LOCKED, and the write-back must be fenced onlease_tokenso a worker that stalled and resumed cannot overwrite a newer attempt.
Follow-up
- One subscription has 4 million queued deliveries. What stops it from starving the other 199,999, and what does your heap look like under that load?
- A customer requests redelivery of last Tuesday's events. Where do those rows enter your structure, and what keeps them from reordering live traffic?
- The process restarts. How much state do you rebuild, and what stops every subscription from being attempted in the same second?
Migrate a live partitioned event table without blocking ingest
usage_event is range-partitioned daily on ingested_at, holds roughly 250M rows per day across 400 live partitions, and is written at 10-40k rows/second. Two changes are required: quantity must move from double precision to numeric(20,6), and a new environment column must become NOT NULL with a default of 'production'. Ingest cannot stop. Give the ordered plan, naming for each step the lock it takes, what that lock blocks, and roughly how long it is held. Identify the one step that cannot be rolled back cleanly once traffic depends on it.
Approach
- Classify the two changes before planning anything. Adding a column with a non-volatile default has been metadata-only since PostgreSQL 11, so it is cheap. Changing double precision to numeric is not binary-coercible, so
alter column ... typerewrites every partition under ACCESS EXCLUSIVE and rebuilds its indexes; on this volume that is hours of blocked ingest and is simply not an option, which is why the plan is expand-and-contract rather than one statement. - Expand: add
quantity_numeric numeric(20,6)andenvironmentwith its default on the parent. Both are catalogue-only but both take a brief ACCESS EXCLUSIVE that cascades to partitions, so run each withlock_timeoutset to a second or two and retry on failure. A queued ACCESS EXCLUSIVE request blocks every reader behind it, which is how a metadata-only change turns into an outage. - Dual-write: deploy producer code that populates both columns on every insert, and leave it running before anything reads the new column. This is the step that cannot be reverted cleanly. Once readers depend on quantity_numeric, reverting the writer leaves rows with a null there, and the gap is only discoverable by re-reading the old column, which the readers have stopped doing.
- Backfill older partitions in batches keyed on the primary key, oldest first, committing every few thousand rows with a pause between batches, and skipping the partition still receiving writes until it rotates. Each batch is an ordinary UPDATE taking row locks only. The cost is bloat and WAL rather than blocking, so watch dead tuples and let autovacuum keep pace instead of wrapping 400 partitions in one transaction.
- Make NOT NULL cheap with the three-step form:
add constraint ... check (environment is not null) not valid(brief ACCESS EXCLUSIVE, no scan), thenvalidate constraint(SHARE UPDATE EXCLUSIVE, scans while reads and writes continue), thenset not null, which from PostgreSQL 12 uses the validated check and skips its own full scan. Do this per partition, then on the parent. - Switch and contract: move reads to the new column behind a flag, verify over a full period that both columns agree on freshly written rows, drop the old column (metadata-only), and only then remove the dual-write. Any index on the new column goes on with CREATE INDEX CONCURRENTLY per partition, since CIC is not supported on a partitioned parent: create the parent index with ONLY, build each child concurrently, then ALTER INDEX ... ATTACH PARTITION until the parent index becomes valid.
Worked solution 45 min
- On a scratch cluster, build 10 partitions of 2M rows each and run a writer at a few thousand inserts/second.
- Run the naive type change and measure how long writes stall and how far ingest lag grows before killing it.
- Run the expand step with
lock_timeout = '2s'while the writer runs, and observe a clean lock timeout and retry instead of a pile-up of blocked readers. - Backfill in 5k-row batches and chart dead tuples and WAL generated per batch.
- Run the not-valid, validate, set-not-null sequence and confirm from
pg_stat_activityand timings that nothing held an exclusive lock through a full scan. - Add an index with CIC per partition plus ATTACH PARTITION and confirm the parent index reports valid only after the last attach.
Follow-up
- A CREATE INDEX CONCURRENTLY fails halfway through the partition list. What state is the table in, how do you detect it, and what do you run?
- The producer computes quantity itself. What happens to a request already in flight when the dual-write deploy lands, and does it matter?
- Give two queries that prove the backfill is complete: one cheap enough to run every minute, one authoritative.
Paginate a tenant's delivery export without skipping rows
A customer exports webhook_delivery: delivery_id (bigint identity), subscription_id, tenant_id, event_id, status, attempt_count, next_attempt_at, created_at, delivered_at, updated_at. The endpoint runs select ... where tenant_id = $1 order by created_at desc limit 100 offset $2, and customers report rows missing from exports taken while new deliveries are being inserted. Write the replacement query and the index that supports it, paging a tenant's deliveries newest first at constant cost per page. State why updated_at cannot be the cursor column.
Approach
- Name the defect precisely. OFFSET is a position in a result set that is recomputed on every request, so a row inserted ahead of the window shifts everything back by one and the next page starts after a row the client never received. Nothing errors and no identifier gap appears, so the loss is silent.
- Replace the position with a value predicate over a stable, unique, indexed ordering:
where tenant_id = $1 and (created_at, delivery_id) < ($2, $3) order by created_at desc, delivery_id desc limit 100. The row comparison is load-bearing: created_at alone is not unique, so ties straddling a page boundary are dropped or repeated, which is the same bug in a smaller window. - Index
(tenant_id, created_at, delivery_id). PostgreSQL scans a btree in either direction, so an all-DESC ORDER BY is served by an ASC index read backwards and no DESC modifiers are needed; they only matter when the ORDER BY mixes directions. Confirm the plan has no Sort node above the index scan, or the LIMIT stops being an early exit. - Price both forms: keyset is one index descent plus 100 adjacent leaf entries per page, constant regardless of depth, while OFFSET still produces and discards every skipped row, so page N costs time proportional to N times the page size and a deep page on a large table goes from milliseconds to seconds.
- Rule out updated_at as the cursor from the precondition, not from taste: a cursor column must never change value for a row already paged past. updated_at moves on every delivery attempt, so a row the client already emitted re-enters a later page and is exported twice. created_at and delivery_id are immutable, which is the whole qualification.
Follow-up
- The client wants a snapshot as of one instant rather than a live tail. Compare a repeatable-read transaction held open, an added
created_at <= $snapshotbound, and a materialised export table. - A retention job deletes deliveries older than 90 days. What does a client mid-walk see, and does keyset pagination help at all?
- The customer wants to resume an export from yesterday's last cursor. What must be true of the cursor for that to be safe?
Can you identify potential performance bottlenecks or logical errors i…
Can you identify potential performance bottlenecks or logical errors in this provided block of code?
Approach
- Work from the requirement backwards to the design.
- 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.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Can you explain the differences and common use cases for Java versus J…
Can you explain the differences and common use cases for Java versus JavaScript in a professional environment?
Approach
- Work from the requirement backwards to the design.
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Write the delivery guarantee and replay API for outbound events
The webhook-delivery service fans events to customer endpoints, tracked in webhook_delivery (subscription_id, tenant_id, event_id, redelivery_seq, status, attempt_count, next_attempt_at, lease_token, last_response_code, payload_digest). Customers are asking for exactly-once and in-order delivery. Write the contract you can actually honour: the guarantee, the headers that make it usable, which customer response codes are retryable, the retry schedule and terminal condition, what happens to an endpoint that has been down for a day, and the shape of the redelivery endpoint. State plainly what you are not promising and what the customer must do instead.
Approach
- Refuse exactly-once with the mechanism rather than with policy: the customer's acknowledgement can be lost after they have already committed, so the sender cannot distinguish unprocessed from processed-but-unacknowledged and must retry. The deliverable is at-least-once with a stable event identifier and the customer documented as the deduplicating party.
- Price ordering instead of promising it. With parallel attempts per subscription, a retried event overtakes a newer one, so in-order delivery requires a single in-flight attempt per subscription, which turns any slow endpoint into head-of-line blocking for that subscription's whole queue. Offer it per subscription with that cost written down.
- Define the response contract from the customer's side: 2xx is accepted, 408, 429 and 5xx are retryable, 410 disables the subscription, and every other 4xx is permanent and terminal. Publish the read timeout and tell the customer to acknowledge first and process asynchronously, since their processing time otherwise consumes your worker occupancy.
- Publish the schedule and its end: capped exponential backoff with full jitter, sleeping uniformly in [0, min(cap, base * 2^attempt)] so a mass failure does not re-synchronise the herd, terminal after a stated attempt count or age, plus a per-endpoint circuit breaker that records further deliveries as dropped_circuit_open and notifies the owner rather than burning shared worker capacity.
- Shape redelivery as an insert, not a reset: POST to a redeliveries collection with event ids or a time window creates rows at redelivery_seq + 1 carrying the same bytes, which payload_digest lets you prove, leaving the original terminal rows intact as the record of what happened.
- Compare the event's tenant against the subscription's tenant at enqueue and again immediately before signing, because cross-tenant delivery originates in an enqueue path that took the subscription from one lookup and the payload from another, not in the worker.
Worked solution 30 min
- Write the guarantee sentence and the one-sentence reason exactly-once is not available over HTTP.
- List the headers a customer needs to build their own dedup table: event id, delivery id, attempt number, subscription id, signature and timestamp.
- Write the response-code table with four classes and the action for each, including the 410 case.
- Write the retry schedule with concrete numbers, the terminal condition, and the breaker's threshold and its effect on the delivery row's status.
- Specify the redelivery request and response, and say which columns change and which do not.
Follow-up
- A customer wants everything they missed during their six-hour outage. Do the dropped_circuit_open rows let you answer that, and what retention bound does the answer depend on?
- You offer the ordered mode and one customer's endpoint slows to two seconds per request. What do their delivery metrics look like, and what do you owe them in the docs?
Invoice detail latency triples after an ORM relationship refactor
An invoice detail endpoint returned in 40 ms at p99 last week. After a refactor replaced a hand-written join with ORM relationship access it returns in 1.4 s, and the regression grows with the number of invoice_line_item rows on the invoice. Database CPU rose, but no statement in the slow-query log exceeds 3 ms. You have request traces with per-span SQL, the ORM statement log, and a staging copy of the data. Produce an ordered diagnostic checklist, the measurement that confirms the cause before any code change, and the fix.
Approach
- Count statements per request before reading any statement duration. A slow-query log hides this class by construction, because every individual query is fast and only their number is wrong; take one trace and count SQL spans.
- Establish proportionality rather than asserting it: sample invoices with 5, 20, 60 and 200 line items and plot statements per request against line count. A straight line of slope 1 through an intercept of one or two identifies a lazy relationship load, and no index or cache would move that line.
- Locate the emitting attribute access in the refactored code and check whether the same shape repeats one level deeper, for instance a tax or adjustment collection hanging off each line, which turns the cost quadratic.
- Fix with a bounded statement count: either one join that fetches invoice and lines together, or two statements where the second is WHERE invoice_id = $1 AND tenant_id = $2. Keep tenant_id in the predicate so the read stays tenant-scoped even though invoice_id already implies it.
- Choose between the two deliberately: the join duplicates the wide parent row across N children on the wire, the two-statement form avoids that for one extra round trip. Prefer the join for narrow parents and the split for wide ones.
- Pin it with a per-request statement-count assertion in a test that varies line count, because a latency assertion passes on a small fixture and would not have caught this.
Follow-up
- The endpoint now also needs per-line tax rows. Show the shape that keeps statement count constant instead of reintroducing the same defect one level down.
- How does this change if a transaction-pooling proxy sits between the service and the database, so each statement may land on a different backend session?
- The same page paginates invoices with LIMIT and OFFSET. Why is that a second, independent defect, and what replaces it?
For a candidate senior enough that the loop turns on design and judgement rather than on whether the coding round gets finished. Five days build one system properly and then stress it; coding gets a single maintenance day, on the assumption that the risk at this level is an unexamined tradeoff rather than a missed algorithm.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Numbers before diagrams
- Build your own reference card of the figures you will re-derive all week: bytes for a realistic record, requests per second implied by a given daily active count, and the storage that a year at a given write rate produces. Derive each one rather than copying it, because the derivation is what survives a follow-up.
- Turn one product statement into capacity requirements. From ten million daily users at four writes and forty reads each, state the peak-to-average factor you are assuming and why, then produce peak write QPS, peak read QPS and a year of storage.
- Write the two numbers whose order of magnitude changes the design, the read-to-write ratio and the working-set size against memory per node, and state the threshold at which each one flips your answer.
Deliverable: A one-page numbers card and one worked capacity estimate with every assumption written down.
Practice prompt ↗Practice prompt ↗Worked solution ↗02One system, from requirements to schema
- Spend the first ten minutes producing only functional requirements, non-functional targets with numbers attached, a p99 latency, a durability expectation, a consistency requirement, and an explicit out-of-scope list.
- Define the interface before the boxes: the three or four endpoints, their parameters, what each returns, and which of them are idempotent.
- Write the data model, then write the single access pattern that justifies it, and state what the schema would have to become if the dominant access pattern were the other one.
Deliverable: One design carried to endpoint-and-schema depth, with non-functional targets expressed as numbers and a written out-of-scope list.
Practice prompt ↗Practice prompt ↗03The consistency you are actually buying
- Write out what a client sees under asynchronous replication when its write commits on the leader and its next read is served by a lagging follower, then write the two fixes, pinning that session's reads to the leader for a bounded window or carrying a version token the replica must reach, and the cost of each.
- Work the quorum arithmetic on paper for N of three with W and R of two, and separate what R + W > N does guarantee, that any read set intersects any write set, from what it does not: on its own it is not linearizability, and a sloppy quorum that accepts writes on nodes outside the preference list breaks even the intersection.
- Take two storage choices with different defaults, a single-leader relational store committing synchronously and a quorum-replicated store that converges eventually, and write the specific product behaviour that would be wrong under each, rather than a general statement about which is stronger.
Deliverable: A page separating what quorum overlap guarantees from what it does not, with one concrete product misbehaviour attached to each gap.
Practice prompt ↗Practice prompt ↗04Failure is the design
- For one write path, work through the case where the client times out after the server has already committed, then design the idempotency key: who generates it, how long it is retained, and what the duplicate request returns.
- Express the retry policy as parameters rather than as a word: maximum attempts, base delay, backoff factor, jitter, and which error classes are retried at all. Then state why retrying a non-idempotent write without a key is a correctness bug and not merely waste.
- Compute the fan-out effect on tail latency. If a request waits on ten backends and each independently exceeds its p99 one percent of the time, the chance at least one is slow is 1 - 0.99^10, about ten percent. Then write why independence is the optimistic assumption and what correlates them in practice.
- Name the backpressure mechanism for one queue or one dependency in the design, a bounded queue with shedding or a concurrency limit, and write what the caller is told when it engages.
Deliverable: One write path with an idempotency design, a parameterised retry policy, and a written tail-latency calculation with its assumption named.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Scaling the hot path
- Choose cache-aside or write-through for one read path and write the staleness window each produces, then name the invalidation event and what the system does when that event is lost.
- Design against the stampede: either coalesce requests so only one recomputes a missing key, or refresh early with jittered expiry, and write why identical TTLs on keys populated in the same moment produce a synchronised expiry and a thundering herd.
- Shard one table by a key you choose, then answer the two questions that break the choice: which queries now require a scatter-gather, and what happens to the distribution when one tenant is a hundred times larger than the median.
- Write the cost of adding a node under plain modulo placement, where nearly every key moves, against consistent hashing, where roughly one key in n+1 moves, and state what virtual nodes are for.
Deliverable: A caching and sharding decision for one path, each with its failure mode and its rebalancing cost written beside it.
Practice prompt ↗Practice prompt ↗06Keep the coding hand in, at the bar that applies to you
- Solve one medium problem in thirty minutes, then spend twenty more making it production-shaped: named invariants, validation at the boundary, and errors that distinguish a caller mistake from an internal fault.
- Write the tests you would require of a colleague's version of that function: one for empty input, one for the boundary, and one for the case the implementation is most likely to get wrong.
- Read a piece of your own code from six months ago and write the change you would ask for, phrased as you would actually phrase it in review.
Deliverable: One problem hardened to review standard, with its test list and one written review comment.
Practice prompt ↗Practice prompt ↗07Defend it while being interrupted
- Run a forty-five-minute design mock with an interviewer briefed to change a requirement halfway, a tenfold traffic increase or a new strict consistency requirement, and to push on one number you estimated.
- Rehearse the two sentences a senior loop is listening for: naming the tradeoff you are choosing against and why, and saying what you would measure to learn that the choice was wrong.
- Prepare the design you regret: a real decision, the constraint that produced it, what it cost, and what you changed afterwards.
Deliverable: Mock notes recording how the design changed under the new requirement, plus a written account of one regretted decision.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
For anything that touched live traffic, be ready to say how you would have undone it: a flag, a staged rollout, dual writes with the old path still authoritative. Once the old column is dropped or the source rows are overwritten there is no reverse, so name what you kept a copy of and for how long.
What is your experience with DevOps practices in a continuous delivery…
What is your experience with DevOps practices in a continuous delivery environment?
Approach
- Name the disagreement and how you resolved it with evidence.
- Give the blast radius: what could have broken, and what you measured.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- What did you decide not to do, and why?
- How did you know your change caused the improvement?
How do you handle code reviews and collaborative feedback loops within…
How do you handle code reviews and collaborative feedback loops within a team?
Approach
- Pick a story where you made the decision, not one where you watched it.
- Name the disagreement and how you resolved it with evidence.
- Close with what you would do differently, concretely.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
Tell me about a time you had to deliver a project under strict time co…
Tell me about a time you had to deliver a project under strict time constraints.
Approach
- Name the disagreement and how you resolved it with evidence.
- 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
- What did you decide not to do, and why?
- How did you know your change caused the improvement?
Describe a time you faced a significant technical roadblock; how did y…
Describe a time you faced a significant technical roadblock; how did you resolve it?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
- 01
What is your experience with DevOps practices in a continuous delivery environment?
- 02
How do you handle code reviews and collaborative feedback loops within a team?
- 03
Tell me about a time you had to deliver a project under strict time constraints.
- 04
Describe a time you faced a significant technical roadblock; how did you resolve it?
Is this an official Open GI interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Open GI. Rounds and questions reflect what candidates have reported, not a process Open GI has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
The difficulty is generally rated as average. The key is to focus on practical, day-to-day coding tasks rather than obscure theoretical puzzles.
PracHub interview research ↗What is the best way to prepare for the live coding round?
Focus on "thinking out loud." The interviewers are looking to understand your logic, so explain your assumptions and your approach before you start writing the code.
PracHub interview research ↗How long does the hiring process take?
The process typically spans three rounds. While timelines can vary, the process is generally structured to move efficiently once you have completed the initial screening.
PracHub interview research ↗What is the work environment like?
Open GI maintains a professional environment where technical standards and structure are prioritized. You should expect a role that values reliability and systematic problem-solving.
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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