As a Software Engineer at Olsson, you play a vital role in building internal digital tooling, automation frameworks, and specialized technical software that powers complex engineering infrastructure projects. Olsson is an employee-owned engineering design firm that handles large-scale projects across site design, power systems, data centers, water resources, and transportation. Software engineers at the firm bridge the gap between traditional engineering disciplines and modern digital platforms, creating custom software solutions that streamline design workflows, manage technical data, and enable sophisticated modeling for internal and client-facing applications. Your work directly impacts the productivity of cross-functional engineering teams and the precision of project deliverables. By developing automation scripts, custom design tools, data integration pipelines, and building management software frameworks, you help optimize complex technical plans across various regional offices. Rather than building consumer-facing mobile apps, Software Engineer roles focus heavily on technical systems integration, domain-specific tooling, and software that supports infrastructure design and data management. Joining the technology team at Olsson offers a unique environment where software development meets physical infrastructure engineering.
Screening Call
reportedA standard call with human resources to discuss your background, interest in the company, and basic qualifications.
What to demonstrate
- A standard call with human resources to discuss your background, interest in the company, and basic qualifications
- Depth in AutoCAD 3D
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Panel Interview
reportedA virtual or in-person interview with engineering managers, team leads, and potential peers to assess technical capabilities and cultural fit.
What to demonstrate
- A virtual or in-person interview with engineering managers, team leads, and potential peers to assess technical capabilities and cultural fit
- Depth in AutoCAD 3D
How to prepare
- Work AutoCAD 3D until you can explain it without notes
- Work Behavioral interview preparation until you can explain it without notes
Informal Conversation
reportedFor higher-level roles, this may include a second informal discussion with regional leadership or direct managers.
What to demonstrate
- For higher-level roles, this may include a second informal discussion with regional leadership or direct managers
- Depth in AutoCAD 3D
How to prepare
- Work AutoCAD 3D until you can explain it without notes
- Work Behavioral interview preparation until you can explain it without notes
Office Tour
reportedA tour of one of the campus offices may be included during the informal conversation stage.
What to demonstrate
- A tour of one of the campus offices may be included during the informal conversation stage
- Depth in AutoCAD 3D
How to prepare
- Work AutoCAD 3D until you can explain it without notes
- Work Behavioral interview preparation until you can explain it without notes
PracHub editorial advice for the preparation topics above.
Prepare concise project stories
Use concrete examples when discussing past team projects. Focus on your specific role, the technical problem, and how you collaborated with others to solve it.
Be ready for personal fit questions
Expect questions about your personal trajectory, such as where you see yourself in 5 to 10 years or what traits you look for in a team lead. Answer openly and authentically.
Highlight cross-functional experience
If you have experience collaborating with non-software professionals (like civil, mechanical, or electrical engineers), highlight those experiences to show you can thrive in an engineering design firm environment.
Communication and response times during the hiring process can sometimes lag
Be sure to send professional follow-ups to your recruiter if you haven't received an update within a week after an interview stage.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Keep one tenant's bulk export from starving projection updates
The worker fleet runs about 600 jobs/second out of job_run, claimed under a lease and heartbeated while running. Most handlers finish under 200 ms; exports run for minutes. One tenant enqueues 50,000 exports. Design the scheduling so that tenant's exports slow down and nothing else does: the queue split, the concurrency caps and where they are enforced, the claim query, and the backpressure signal that stops accepting new work. State the in-flight worker count each class needs at its arrival rate, and what the cap costs the tenant that hits it.
Approach
- Do the concurrency arithmetic first, because it determines the pool shapes. Required in-flight work is arrival rate times service time: 600/second of 200 ms handlers needs about 120 workers, while one export per second at 180 seconds needs 180 on its own. The long tail dominates any pool it shares, so a single pool sized from the mean is consumed by exports while 50 ms projections queue behind them. That is head-of-line blocking, and adding capacity does not fix it because the ratio is what is wrong.
- Split by job class rather than by priority. Priority inside one pool still lets a running export hold its worker for the next four minutes - there is no preemption for a handler already executing. Separate queues and separate worker processes give each class a floor the other cannot take.
- Enforce the per-tenant cap inside the claim, not at enqueue. Claim with SELECT ... WHERE status='queued' AND run_after <= now() AND job_type=$1 ORDER BY run_after, job_run_id FOR UPDATE SKIP LOCKED LIMIT 1, restricted to tenants currently under their cap. Derive the running count from rows in status 'running' with a live lease rather than from a counter incremented at claim and decremented at completion: the derived count self-heals when a worker dies, and the counter leaks a slot every time one does.
- Make the ordering fair over tenants instead of over jobs. Strict FIFO with a cap still forces the claim to scan past 50,000 rows belonging to a capped tenant before it finds anyone else's work, so cost grows with backlog depth. Pick the tenant first from a small per-tenant queue-depth summary, then claim within that tenant, which makes the scan proportional to the number of active tenants instead.
Follow-up
- An export outruns its lease, so two copies run. What must the handler do to make the second copy harmless, and what does it write to guarantee that?
- With a cap of 4, the tenant's 50,000 exports now take hours. What do you tell them, and does the answer change if they are the largest tenant on the platform?
Give the resource write endpoints a failure taxonomy clients can act on
Two callers use POST and PATCH /v1/resources: a server-side SDK that retries automatically, and a browser app that shows the user a message. Today every failure is a 500 carrying prose. Define the error contract for a malformed body, a field that fails validation, an expired token, a token whose auth_version no longer matches, a resource_id owned by another tenant, a version mismatch on update, an idempotency key reused with a different body, an exceeded tenant quota, and a read replica that has not caught up. Deliverable: the envelope, the status per case, and what each caller does.
Approach
- Split the envelope by audience: a stable
codeenum for programs, amessagedocumented as human-only and free to change wording, arequest_idthat joins to resource_revision.request_id and the trace, and adetailsarray of field paths for validation failures. Publish the negative rules too - clients never branch onmessage, and an unrecognisedcodefalls back to the status class. - Assign status by who has to change something. 400 for bytes that do not parse, 422 for a body that parses and violates a rule, 401 for a token that no longer authenticates - expiry and an auth_version mismatch are the same instruction, re-authenticate - 403 for a scope the principal lacks, 404 rather than 403 for another tenant's resource_id because 403 confirms the id exists, 412 for a failed If-Match (409 if the version travels in the body instead), 422 for a key reused with a different fingerprint, 429 for quota.
- Derive retryability from whether the outcome is unknown, not from the status number. A timeout or a 5xx on a write is unknown - the transaction may have committed and the response lost - so the only safe retry is one carrying the same idempotency key. Every 4xx except 429 is deterministic, and retrying it only spends the caller's remaining deadline.
- Refuse to model replica lag as an error. Read-after-write is held by pinning the session to the primary for a short window, not by a 404 the SDK retries into a loop; if staleness must be visible, expose it as a watermark on a 200, because a failure code invites a retry that cannot fix it.
Follow-up
- A customer reports two resources created from one click. Which of your status codes could have produced that, and what in the contract permitted the client's reading?
- You need a tenth error code next quarter without a version bump. What must v1 already have said for that to be non-breaking?
Listing latency scales with page size, not with filters
The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.
Approach
- Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
- Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
- Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
- Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.
Follow-up
- The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
- How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?
Built from the rounds and topics Olsson candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Olsson loop
- Write out the reported sequence: Screening Call, Panel Interview, Informal Conversation, Office Tour.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work AutoCAD 3D
- Spend the session on AutoCAD 3D, which Olsson candidates report being tested on.
- Write one worked example in AutoCAD 3D and time yourself on it.
Deliverable: One timed worked example in AutoCAD 3D.
03Work Behavioral interview preparation
- Spend the session on Behavioral interview preparation, which Olsson candidates report being tested on.
- Write one worked example in Behavioral interview preparation and time yourself on it.
Deliverable: One timed worked example in Behavioral interview preparation.
04Work Power systems studies (data centers / SCCAF)
- Spend the session on Power systems studies (data centers / SCCAF), which Olsson candidates report being tested on.
- Write one worked example in Power systems studies (data centers / SCCAF) and time yourself on it.
Deliverable: One timed worked example in Power systems studies (data centers / SCCAF).
05Consolidate
- Re-work the problem you got wrong earliest in the week, from scratch, without looking at your previous attempt.
Deliverable: A second, cleaner solution to the problem you got wrong first.
06Rehearse your own examples
- Prepare three examples from your own work where you made the decision, each with the outcome you can quantify.
Deliverable: Three examples written out, each with a number attached.
07Dry run for Olsson
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Narrate an outage you owned from page to postmortem
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
Approach
- Open on the signal rather than the cause: which metric at which percentile moved, on which service, at what time, so the listener follows the same evidence you had rather than a conclusion you already reached.
- Separate mitigation from diagnosis out loud. State what you did to stop the bleeding (flag off, shed traffic, drain a lease, roll back a deploy) and say plainly that you did it before the mechanism was known, because those are two jobs with different deadlines.
- Establish blast radius in countable terms: how many tenants, how many writes, and crucially whether the effect was loss or only delay. An append-only revision table or a pending outbox row means the change survived and the projection was merely behind, which is a repair rather than a data-loss incident.
- Prove the mechanism instead of asserting it. Name the trace span that grew, the plan that flipped to a sequential scan, the lease that expired, plus one alternative you ruled out and the signal that stayed flat while you ruled it out.
Follow-up
- What would you do differently in the first five minutes, given the same dashboard and no more information?
- Which follow-up action did you deliberately not take, and why was dropping it the right call?
- 01
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
- 02
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 03
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
How difficult are technical interviews for Software Engineers at Olsson?
Most candidates report that interviews at Olsson are average or easy in technical difficulty. The focus is significantly more on your background, project experience, and interpersonal fit than on whiteboard algorithm puzzles.
Olsson Software Engineer candidate reports ↗What is the interview format like?
The process typically consists of an initial HR phone screening followed by a virtual or in-person panel interview with team members and managers. The panel discussion often feels like a peer-level conversation about your background and past projects.
Olsson Software Engineer candidate reports ↗Does Olsson ask about personal background and hobbies?
Yes. Olsson places strong emphasis on company culture and personal rapport. You should expect questions about your hobbies, personal growth, and what drives you outside of technical work.
Olsson Software Engineer candidate reports ↗How quickly does Olsson extend an offer after interviews?
Offer timelines vary by location and team.
Olsson Software Engineer candidate reports ↗Are remote work options available?
Olsson offers a mixture of in-office, hybrid, and remote roles depending on the specific team, regional office, and project scope.
Olsson Software Engineer candidate reports ↗What topics does Olsson test in interviews?
Olsson interviews most often cover Cross-functional Collaboration, Project Management, Marketing Analytics, AutoCAD 3D, and Engineering Project Management. The exact emphasis depends on the specific role you apply for.
Olsson Software Engineer candidate reports ↗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.
- 01Olsson Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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