As a Software Engineer at Isar Systems and Technologies Private, you are at the heart of an ambitious mission to redefine access to space. This role is not merely about writing code; it is about building the robust, high-reliability software systems that control complex aerospace hardware. Your work directly impacts how launch vehicles navigate, how fluid systems are managed, and how critical flight data is processed.
You will contribute to a fast-paced environment where the gap between code and physical reality is incredibly small. Whether you are working on Avionics, Mission Analysis, or Process Design, your contributions ensure that the software is as resilient as the rocket itself. This position is critical because you are bridging the gap between cutting-edge engineering theory and operational performance in a high-stakes, innovative industry.
Expect to work in a collaborative, cross-functional setting. You will interface with mechanical engineers, propulsion experts, and systems architects to ensure that your software solutions integrate seamlessly with the hardware they control. Success here requires a blend of deep technical rigor, a hands-on approach to problem-solving, and a genuine passion for the challenges inherent in aerospace engineering.
HR Screening
reportedHalf of this call is the part candidates treat as small talk: start date, notice period, work authorisation and its timing, location and time zone, on-call, and the number. Those are what kill offers late, after several engineers have each spent a day. Surfacing a hard constraint now costs you nothing and occasionally buys you something, since a loop compressed to fit a competing deadline can usually only be arranged if it is asked for early. The common failure is deflecting the compensation question twice, then discovering at offer stage that the band never reached your number.
What to demonstrate
- Whether your hard constraints are compatible with the role before a loop gets booked: earliest start, notice period, what authorisation you hold and when it needs action, days on site, willingness to carry a pager
- Whether you give a compensation range with something behind it, such as current total compensation or a competing timeline, rather than leaving the band untested
- Whether your stated timeline is real, since a competing deadline raised now is something scheduling can sometimes work around and the same deadline raised at offer stage usually is not
How to prepare
- Write each constraint down in one line before the call and state them as facts rather than negotiating them live under a question you were not expecting
- Set your range from two or three current data points for that level and location, and name the structure you are quoting in, so the number is comparable to the one they are holding
- If another process is running, say where it stands and by when, and ask directly whether this loop can be scheduled inside that window
Technical Assessments
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
Interviews with Hiring Team
reportedWhen a round has no standard shape, it is often there because something is still open: an area no earlier conversation reached, a round where the signal came out mixed, or a decision someone is not ready to make alone. Work out which by going back over what each earlier round actually covered rather than how it felt, and arrive able to give evidence on that point without being asked twice. Weak answers replay the loop's earlier material at the same depth. Strong ones go a level deeper and stay consistent with what you already said.
What to demonstrate
- Whether your account of a project matches the one you gave earlier in the loop, since what you said before may be available to whoever runs this round
- Whether you can go a level deeper on something already covered, reaching the decision and its alternatives rather than repeating the summary
- Whether you state your own uncertainty accurately, including parts of a system you did not build and decisions you inherited, instead of claiming even ownership across all of it
- Whether you can answer a question you handled poorly earlier by naming what you missed, rather than delivering a polished second version as if the first had not happened
How to prepare
- Reconstruct the loop on one page: for each round, the questions you were asked and the answer you actually gave, not the better one you thought of afterwards. The gaps on that page are your best available guess at why this round exists.
- Take the two claims you made earlier that carry the most weight and assemble the backing for each: the measurement, the date, what broke, the decision you would make differently now.
- Write down the three facts about your work that must not drift between tellings, such as team size, timeline and your own role, and check your stories against that list rather than trusting recall under pressure
Executive-Level Discussion
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
PracHub editorial advice for the preparation topics above.
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.
One shared connection pool for every tenant and every query class
A single tenant with a large table and a missing index can occupy every connection with slow queries, and every other tenant then waits in connection acquisition -- a queue invisible in database metrics, because the database itself looks healthy while the application starves. Containment is bulkheads: separate pools or per-tenant concurrency caps for interactive requests, background jobs and exports, a statement timeout low enough that a pathological query dies before it accumulates, and an idle-in-transaction timeout so a stuck client cannot pin a connection and its locks indefinitely. One caveat worth knowing in advance: if a transaction-pooling proxy sits in front of the database, session-scoped behaviour changes, so session-level advisory locks and settings applied outside a transaction do not survive the way they do on a direct connection.
Going silent while thinking
Narrate the candidates and why you are discarding them, even in fragments: sorting first would make this a two-pointer scan, but it destroys the original indices, which the output needs. From the other side of the table, a candidate thinking hard and a candidate stuck are indistinguishable until one of them speaks.
Quoting amortised or average cost as if it were a worst-case guarantee
Appending to a dynamic array is amortised O(1), but the append that triggers a resize copies every element, and hash lookup is constant only while the hash spreads the actual keys. Say which guarantee you are offering when the caller cares about the latency of one call rather than the total over many.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Order a job dependency graph and find its critical path
A workspace defines up to 50,000 jobs with up to 200,000 dependency edges and an estimated duration_seconds per job. Given the edge list, reject the graph if it contains a cycle and name one cycle's nodes; otherwise return a valid execution order, the earliest possible completion time with unlimited workers, and the set of jobs whose slack is zero. Then say which single job to shorten in order to cut the completion time, and by exactly how much. State the complexity of each part.
Approach
- Kahn's algorithm for the order: compute indegrees, seed a queue with zero-indegree nodes, emit and decrement. O(V + E), which at 50,000 and 200,000 is milliseconds. If fewer than V nodes are emitted, the graph contains a cycle.
- Kahn detects a cycle but cannot name one. The nodes left with indegree above zero contain every cycle, so run one DFS restricted to that residual subgraph with three-colour marking and report the stack slice from the grey node the back edge points at. That is the difference between a usable error message and 'dependency cycle detected'.
- Earliest completion with unlimited workers is the longest path, which is NP-hard on a general graph and linear on a DAG. State the precondition, then relax in topological order:
earliest_finish[v] = duration[v] + max(earliest_finish[u] for u in preds(v)), taking the max over an empty predecessor set as zero. The makespan T is the maximum over all nodes. O(V + E). - Second pass in reverse topological order for
latest_finish, thenslack[v] = latest_finish[v] - earliest_finish[v]. Zero-slack nodes form the critical path, and there can be several disjoint critical paths, so return the set rather than one chain.slack[v] = 0is exactly the statement that some longest path runs through v; equivalently, the longest path through v has lengthT - slack[v]. - The speed-up bound is the point of the question, and the obvious form of it is wrong. Shortening a zero-slack job v by d, with 0 <= d <= duration[v], cuts the makespan by
min(d, T - L_avoid(v)), whereL_avoid(v)is the longest path in the graph with v deleted: the longest path that avoids v, not the second-longest path overall. The two coincide only when the runner-up path misses v. Counterexample: A of 10 s feeds both B of 5 s and C of 4 s, so T = 15 s and the second-longest path is 14 s, yet shortening A by 10 s leaves a makespan of 5 s. The realised gain is the full 10 s, because both paths ran through A and shrank together, whilemin(10, 15 - 14)predicts 1 s. The reason is structural: shortening v reduces every path through v by d and leaves every other path alone, so the new makespan ismax(T - d, L_avoid(v)). - Compute
L_avoid(v)the direct way: delete v and re-run the same forward relaxation, O(V + E) per candidate. The cheaper equivalent skips the deletion, sinceL_avoid(v)only ever matters through that max: setduration[v] := 0, recompute the makespan asT0(v) = max(T - duration[v], L_avoid(v)), and the gain ismin(d, T - T0(v)), which is identical for every d <= duration[v]. Only zero-slack jobs are candidates, because shortening a job with positive slack changes the completion time not at all. One relaxation is milliseconds at this size, so ranking a critical set in the hundreds costs O(k(V + E)) and is worth doing exactly; a critical set in the tens of thousands is not, and there you evaluate a shortlist, longest jobs first, and say that the answer is the best of that shortlist rather than the optimum.
Follow-up
- Only m workers are available. What happens to your answer, and what can you still promise about the schedule you produce?
- Edges arrive incrementally as the customer edits the pipeline. How do you detect a cycle at insert time without re-running Kahn over 250,000 elements?
- Durations are estimates. How would you express completion time as a distribution, and what breaks about the critical path once you do?
Parse and verify a timestamped multi-signature webhook header
An inbound webhook carries a signature header of at most 1 KiB shaped t=<unix seconds>,v1=<64 hex chars>, with up to five v1 values during secret rotation and possibly unknown scheme keys. You hold the raw request body bytes and the currently active signing secrets. Write the parser and the verifier: accept when any active secret reproduces a signature and the timestamp is within a five-minute tolerance in either direction, reject otherwise. Single left-to-right pass over the header, no regular expression. State what is inside the MAC and why.
Approach
- Parse in one scan: split on
,, then on the first=only, since a value may itself contain=under a future scheme. Accepttexactly once and treat a secondtas a reject rather than last-wins. Push everyv1onto a short list and ignore any other key, so av2can be introduced later without breaking this verifier. - Say what is signed: HMAC-SHA256 over the exact byte string
<t>.<raw body bytes>, yielding 32 bytes or 64 hex characters. The timestamp sits inside the MAC because otherwise an attacker replays yesterday's body with its still-valid signature and only has to edit the header timestamp. - Hash the bytes as received. Verifying against a re-serialised JSON body is the usual defect: key order, whitespace and number formatting all change the bytes while the parsed objects compare equal, so signatures fail for honest senders and the popular 'fix' is to stop checking.
- Compare in constant time over fixed-length digests. Decode the hex to 32 bytes, accumulate
acc |= a[i] ^ b[i]across the whole length, and testacc == 0at the end. Evaluate every candidate without an early exit; at five candidates that is five HMACs over the body, linear in body size and negligible beside the network. - Apply the tolerance as a two-sided bound, rejecting when
|now - t| > 300seconds. A sender whose clock runs ahead of yours is an ordinary case, and an unbounded future timestamp is a free replay window. - Complexity: O(L) over the header producing k candidates, plus k HMACs at O(|body|) each. Space is O(k) beyond the body itself. Do the cheap rejections, including the tolerance check, before any cryptography runs.
Follow-up
- The body is 40 MB. What changes about where you verify, and what can you do before the whole body has arrived?
- A customer reports that signatures fail for exactly the requests whose body contains a non-ASCII character. What is your first hypothesis?
- How do you rotate the signing secret with no failed deliveries, and how long do both secrets stay live?
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.
Worked solution 30 min
- Define the four structures explicitly:
queues: subscription_id -> deque[delivery],ready: min-heap of (next_attempt_at, subscription_id),inflight: set[subscription_id],breaker: min-heap of (half_open_at, subscription_id). - Write down the invariant you will assert after every operation: a subscription appears in at most one of
ready,inflightandbreaker, never in two. - Implement
due,completeandinsert, then simulate 200,000 subscriptions with Zipf-distributed queue depths totalling 20 million deliveries. - Add one endpoint that always times out after 10 seconds and one that always answers in 20 ms, then measure the fast endpoint's throughput with and without the per-endpoint breaker.
- Instrument heap size across the run.
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?
Explain why the metering dashboard scans every daily partition
usage_event is range-partitioned daily on ingested_at and holds tenant_id, workspace_id, environment, sku, quantity numeric(20,6), occurred_at and ingested_at. The only relevant index is on (occurred_at). A dashboard runs select sku, sum(quantity) from usage_event where tenant_id = $1 and date_trunc('hour', occurred_at) >= $2 and environment = 'production' group by sku, and EXPLAIN shows a sequential scan of every partition. Give each distinct reason, rewrite the predicate so an index can serve it, propose the index, and state the write cost its column order adds.
Approach
- Separate the three causes rather than blaming one. First,
date_trunc('hour', occurred_at)wraps the column, so the predicate is not sargable against a btree on the bare column. Second, pruning keys off ingested_at while the query constrains occurred_at, so no partition can be excluded. Third, even made sargable, (occurred_at) is not tenant-leading, so for one tenant among thousands the scan reads the whole time range and discards nearly all of it. - Rewrite the bound carefully, because the obvious rewrite is only conditionally equivalent.
date_trunc('hour', x) >= $2equalsx >= $2only when $2 is already hour-aligned; for an arbitrary $2 it meansx >= date_trunc('hour', $2) + interval '1 hour'. Normalise the parameter in the caller and leave the column bare. - Restore pruning with a second, redundant predicate on the partition key:
ingested_at >= $2 - interval '<late-data horizon>'. State both sides of it. It prunes to a handful of partitions, and it silently omits any event whose ingest lagged past that horizon, which is precisely what a producer replay produces. Either document the horizon as a stated bound, or partition on occurred_at and move the problem into the dedup window instead. - Propose
(tenant_id, occurred_at) include (sku, quantity)per partition. A partial indexwhere environment = 'production'mostly saves size rather than selectivity, since production dominates the three environments; take it if non-production is a meaningful share and skip it otherwise. - Price the write path honestly. At roughly 250M rows/day each extra index is another insert plus WAL per row, and a tenant-leading key scatters inserts across one hot leaf per active tenant instead of appending to a single rightmost leaf, so page dirtying and random I/O both rise. An INCLUDE payload widens every leaf entry and enlarges the index accordingly.
- Add the index-only-scan caveat before someone reports it as a regression: on a freshly appended table the visibility map is not yet set for recent pages, so the INCLUDE columns still cost heap fetches until autovacuum has been through, and the newest hour is exactly the data the dashboard reads.
Worked solution 30 min
- Build 30 daily partitions with skewed tenants, one holding about 40% of the rows, then ANALYZE.
- Run
explain (analyze, buffers)on the original query and record how many partitions were scanned and the rows removed by filter. - Apply the rewritten predicate and the index, re-run, and confirm the plan lists only the partitions inside the ingested_at bound.
- Re-run with $2 set to a non-hour-aligned timestamp and confirm the rewritten and original predicates return identical rows.
- Insert an event with ingested_at six hours past occurred_at and check whether the pruning predicate excludes it.
Follow-up
- CREATE INDEX CONCURRENTLY is not supported on a partitioned parent. Give the sequence that gets this index onto 400 existing partitions without blocking ingest.
- One tenant holds 200 times the median row count and the dashboard still times out for them with the index in place. What changes?
- Should this read hit
usage_rollup_hourlyinstead? State what that costs in freshness and what the watermark lets you promise.
Model credential revocation so history survives the delete
tenant_api_key stores key_id, tenant_id, workspace_id, name, key_prefix, secret_hash, scopes text[], status (active, revoked, expired, compromised), auth_version, created_at, expires_at, last_used_at, revoked_at, revoked_reason. Rotation inserts a new row and revocation never deletes, because an incident review asks which credential served a request last quarter. Write the constraints that enforce: a label is unique only among a tenant's live keys, revoked_at and status can never disagree, and scopes is never empty. Then write the authentication lookup predicate, and name one column in this table that must stay out of it.
Approach
- Reach for a partial unique index rather than a plain UNIQUE:
create unique index on tenant_api_key (tenant_id, name) where revoked_at is null. Any number of revoked rows may share a label, the live namespace stays unique per tenant, and the revoked majority is not in the index at all, so it stays small on a table that only grows. - Tie the nullable timestamp to the enum so the two cannot drift:
check ((revoked_at is not null) = (status in ('revoked','compromised')))andcheck ((revoked_at is null) = (revoked_reason is null)). A revocation that records no reason is the one an incident review cannot use. - Write the emptiness check as
check (cardinality(scopes) > 0), notarray_length(scopes, 1) > 0. array_length returns NULL for an empty array, a CHECK constraint passes when its expression is NULL, so the array_length version accepts exactly the value it was written to reject. - Make the lookup a single index probe with every liveness condition inside it:
where secret_hash = $1 and revoked_at is null and (expires_at is null or expires_at > now()) and auth_version = $2, backed by a unique index on secret_hash. Nothing is filtered in application code, so there is no path that forgets a clause. - Keep last_used_at out of that predicate. It is written asynchronously and is allowed to lag by a minute, so it is a usage signal; feeding it into an authorisation decision makes the decision depend on a write that may be late, batched away or lost.
- Flag the modelling smell while you are here:
expiredis derivable fromexpires_at < now(), so storing it as a status obliges a job to keep it true and guarantees the column is wrong between the expiry instant and that job's next run. Derive it in the predicate; keep the stored status for states that are decisions rather than clock readings.
Follow-up
- Rotation issues a replacement while the old key stays live for a 30-day overlap. What does the uniqueness rule become, and what does the UI show to tell two same-named keys apart?
- A password reset bumps the principal's auth_version. No row in this table changed. How does the next request fail, and what query counts how many keys that bump just killed?
- A key turns up in a public repository. Which columns let you find it, and what do you write to the row?
How would you handle specific technical problems during development?
How would you handle specific technical problems during development?
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What is a cryogenic propellant?
What is a cryogenic propellant?
Approach
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Design a machine-readable error contract for the gateway
The edge gateway serves roughly 30k requests/second to SDKs and CI pipelines that retry automatically. Today every failure returns 500 with a prose message that clients string-match on. Design the error contract: the response body fields, and the status code for a malformed body, a revoked credential, a scope the credential lacks, a row belonging to another tenant, a reused idempotency key sent with a different body, an exceeded rate limit, and an unreachable dependency. For each, state whether the client may retry and on what schedule. Deliverable: the envelope schema plus the status-to-retry table.
Approach
- Split the envelope by audience: a stable
codestring for programs, amessagedocumented as human-only and free to change, arequest_idthat joins to gateway logs, and adetailsarray for per-field problems. The code list is an enum that only ever grows. - Assign status by who has to change something: 400/422 for the caller's bytes, 401 for a credential that no longer authenticates, 403 for a scope or entitlement, 404 rather than 403 for a row in another tenant because 403 confirms the identifier exists, 409 for an idempotency conflict, 429 for a limit, 503 for a dependency.
- Derive retryability from the method and the idempotency key rather than from the status: a 5xx or a timeout is an unknown outcome, not a failure, so GET/PUT/DELETE may be retried under HTTP semantics and POST only when it carries an idempotency key.
- Put the schedule in the response: Retry-After on 429 and 503 overrides the client's own backoff; otherwise capped exponential backoff with full jitter, sleeping uniformly in [0, min(cap, base * 2^attempt)], bounded by a total attempt budget so retries expire before the caller's deadline.
- Write the negative rules into the published contract: clients must never parse
message, must tolerate unknowncodevalues by falling back to the status class, and a code's meaning is never redefined once shipped.
Worked solution 20 min
- Write the envelope as a JSON schema with four top-level fields and say which are guaranteed present on every error.
- Fill a seven-row table: condition, status, code string, retryable yes/no, and the schedule or the reason retrying cannot help.
- For each non-retryable row, write the one thing the caller must change (bytes, credential, plan, key) so nothing is marked non-retryable without a remedy.
- Add the unknown-outcome row for timeouts and 5xx separately from the other rows, and give it an action other than 'treat as failed'.
- Write two sentences of client guidance: honour Retry-After when present, apply full jitter otherwise, and stop at the attempt budget.
Follow-up
- A customer reports they retried a 500 from POST /v1/runs and ended up with two sandboxes billed. Whose bug is it, and what in your contract permits their reading?
- You need to add a new error code next quarter without a version bump. What did the v1 contract have to say for that to be non-breaking?
One tenant's counter writes stall the whole connection pool
A change that made a per-tenant usage counter correct now produces site-wide latency whenever one large tenant writes: unrelated endpoints time out waiting for a connection while database CPU stays low and no statement is slow. The change wraps the counter update in a transaction that takes SELECT ... FOR UPDATE on one row, calls an external pricing service, then updates and commits. Give an ordered checklist, the arithmetic that bounds that tenant's write rate, and three repairs with the cost each one accepts.
Approach
- Separate waiting from working. Low database CPU alongside high application latency points at a queue, so instrument connection-acquisition wait separately from query execution time; that queue forms in the application and is invisible in database metrics, which is why the database looks healthy throughout.
- Confirm the lock rather than assuming it: sample waiting sessions and group by wait event, relation and tuple. Contention concentrated on one tuple belonging to one tenant is the signature; a deadlock would instead show the database aborting transactions after its detection timeout, which is not happening here.
- Do the arithmetic out loud. Throughput on a serialised row is one divided by the lock hold time, and the hold spans the external call, so a 20 ms pricing call caps that tenant near 50 writes per second no matter how many pods run. Every waiter also holds a pooled connection while it queues, so the shared pool drains and unrelated tenants fail at acquisition.
- Repair one: shrink the critical section to a single statement with the price resolved before the transaction opens. Cost is a stale price for the duration of one request and a second round trip; benefit is a hold time measured in the database's own execution time.
- Repairs two and three change where the contention lives rather than how long it is held. Sharding the counter into per-(tenant, bucket) rows and summing on read multiplies write throughput by the shard count, at the cost of an aggregate on every read and a shard count you must size against the largest tenant rather than the median. Accumulating in memory and flushing periodically removes the per-write round trip entirely, paid for with a bounded loss window on crash, which is acceptable for a rate limiter and not for a billing counter.
- Contain independently of which repair wins: a separate pool or per-tenant concurrency cap for this write class, a statement timeout low enough that a pathological query dies before it accumulates waiters, and an idle-in-transaction timeout so a stuck client cannot pin a connection and its locks.
Follow-up
- What would a genuine deadlock look like here, which two code paths would produce one, and how does the database's response differ from what you observed?
- If a transaction-pooling proxy sits in front of the database, which of your three repairs changes behaviour, and what stops working that would have worked on a direct connection?
- The counter also enforces a quota. Why is SELECT the count and then INSERT still wrong after you have fixed the contention?
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.
Counting review comments or mentees proves nothing. The useful version is a specific change you approved with a reservation you stated, or one you blocked and the delay that cost. Say which standard you were holding and why it was worth the friction. A mentoring story needs the thing the other person can now do without you.
Describe a time you had to take a difficult decision at work.
Describe a time you had to take a difficult decision at work.
Approach
- 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.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
What do you expect from your team, and what requirements do you have f…
What do you expect from your team, and what requirements do you have for them?
Approach
- Give the blast radius: what could have broken, and what you measured.
- 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?
Why do you want to work with us?
Why do you want to work with us?
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 did you decide not to do, and why?
- What would you do differently if you ran that again?
Tell us about yourself and your projects without going into technical …
Tell us about yourself and your projects without going into technical details.
Approach
- Give the blast radius: what could have broken, and what you measured.
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
- 01
Describe a time you had to take a difficult decision at work.
- 02
What do you expect from your team, and what requirements do you have for them?
- 03
Why do you want to work with us?
- 04
Tell us about yourself and your projects without going into technical details.
Is this an official Isar Systems and Technologies Private interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Isar Systems and Technologies Private. Rounds and questions reflect what candidates have reported, not a process Isar Systems and Technologies Private has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long does the interview process typically take?
The process is generally efficient, often moving from application to final decision within 3–4 weeks.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates are those who demonstrate both technical rigor and a genuine interest in the "why" behind their work. Being able to explain your technical decisions clearly and showing an ability to work collaboratively are key differentiators.
PracHub interview research ↗How much preparation time is typical?
Because the process includes both technical assessments and behavioral interviews, most candidates find that 1–2 weeks of focused preparation is appropriate, depending on their familiarity with the specific domain.
PracHub interview research ↗Is the atmosphere formal or informal?
Candidates often describe the environment as professional yet warm and friendly. While the work is serious, the interview style is often conversational.
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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