As a Software Engineer at Whalar Group, you are tasked with building the infrastructure that powers the creator economy. This role is central to the company’s mission of connecting brands with creators through technology, requiring you to navigate complex architectural challenges, scalable data systems, and the evolving landscape of digital marketing. You are not just writing code; you are solving real-world problems that directly impact the efficiency and reach of global influencer marketing campaigns. The work environment at Whalar Group is characterized by high technical standards and a collaborative culture. You will work closely with product managers, team leads, and the CTO to translate business needs into robust, maintainable software. Whether you are working with PHP, Java, Python, or Go, your focus will be on delivering high-quality, scalable solutions that support a fast-paced, innovative product ecosystem. ##### Tip The team highly values candidates who demonstrate a pragmatic approach to problem-solving. They are more interested in your ability to think through architectural trade-offs than your ability to memorize syntax.
Initial Screening
reportedEngage in preliminary conversations to assess general fit for the role.
What to demonstrate
- Engage in preliminary conversations to assess general fit for the role
- Depth in Software architecture
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.
Take-Home Assignment
reportedComplete a critical, time-intensive assignment that reflects your technical approach.
What to demonstrate
- Complete a critical, time-intensive assignment that reflects your technical approach
- Depth in Software architecture
How to prepare
- Answer aloud and timed: How do you balance the trade-offs between different database technologies for a new microservice?
- Answer aloud and timed: Describe a situation where you had to refactor a legacy system; what was your strategy?
Technical Leadership Interview
reportedParticipate in in-depth discussions with technical leadership to evaluate architectural capabilities.
What to demonstrate
- Participate in in-depth discussions with technical leadership to evaluate architectural capabilities
- Depth in Software architecture
How to prepare
- Answer aloud and timed: How do you approach testing, specifically in the context of TDD or automated integration testing?
- Answer aloud and timed: Tell me about a time you had a technical disagreement with a colleague; how did you resolve it?
Final Review
reportedReview and discuss the outcomes of your technical work and overall fit for the team.
What to demonstrate
- Review and discuss the outcomes of your technical work and overall fit for the team
- Depth in Software architecture
How to prepare
- Answer aloud and timed: How do you prioritize your work when faced with multiple competing deadlines?
- Answer aloud and timed: Describe a complex technical challenge you faced and the steps you took to overcome it.
PracHub editorial advice for the preparation topics above.
Communicate your thought process
If you are unsure about a technical question, it is better to explain your reasoning than to stay silent. The interviewers value honesty and critical thinking.
Ask questions
Use your time with the CTO and team leads to ask about their development philosophy and the company's long-term technical vision.
Respect the assignment guidelines
If you are given a specific task, follow the instructions regarding testing and documentation closely, as these are key evaluation criteria.
Be prepared for behavioral questions
The team wants to know how you work within a team, so have examples ready that highlight your collaboration and conflict-resolution skills.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Merge partitioned event streams into one ordered feed with bounded lateness
The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.
Approach
- Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
- Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
- Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
- Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.
Follow-up
- The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
- The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?
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?
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Can you explain your process for identifying and mitigating potential security vulnerabilities like SQL inject
Can you explain your process for identifying and mitigating potential security vulnerabilities like SQL injection?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
How would you approach designing a scalable REST API for a high-traffic platform?
How would you approach designing a scalable REST API for a high-traffic platform?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you balance the trade-offs between different database technologies for a new microservice?
How do you balance the trade-offs between different database technologies for a new microservice?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe a situation where you had to refactor a legacy system; what was your strategy?
Describe a situation where you had to refactor a legacy system; what was your strategy?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you approach testing, specifically in the context of TDD or automated integration testing?
How do you approach testing, specifically in the context of TDD or automated integration testing?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Edge instances grow 400 MB per hour until the nightly restart
Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.
Approach
- Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
- Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
- Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
- The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.
Follow-up
- Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
- How would you have detected this before an OOM, given the nightly restart masked the trend?
Built from the rounds and topics Whalar Group candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Whalar Group loop
- Write out the reported sequence: Initial Screening, Take-Home Assignment, Technical Leadership Interview, Final Review.
- 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 Software architecture
- Spend the session on Software architecture, which Whalar Group candidates report being tested on.
- Write one worked example in Software architecture and time yourself on it.
Deliverable: One timed worked example in Software architecture.
03Work API development (REST)
- Spend the session on API development (REST), which Whalar Group candidates report being tested on.
- Write one worked example in API development (REST) and time yourself on it.
Deliverable: One timed worked example in API development (REST).
04Work Security: SQL injection prevention
- Spend the session on Security: SQL injection prevention, which Whalar Group candidates report being tested on.
- Write one worked example in Security: SQL injection prevention and time yourself on it.
Deliverable: One timed worked example in Security: SQL injection prevention.
05Answer out loud: Technical Architecture and Design
- Answer aloud, timed: How would you approach designing a scalable REST API for a high-traffic platform?
- Answer aloud, timed: Can you explain your process for identifying and mitigating potential security vulnerabilities like SQL injection?
Deliverable: Spoken answers to 2 reported Technical Architecture and Design question(s), under time.
06Answer out loud: Behavioral and Problem Solving
- Answer aloud, timed: Tell me about a time you had a technical disagreement with a colleague; how did you resolve it?
- Answer aloud, timed: How do you prioritize your work when faced with multiple competing deadlines?
Deliverable: Spoken answers to 2 reported Behavioral and Problem Solving question(s), under time.
07Dry run for Whalar Group
- 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.
Tell me about a time you had a technical disagreement with a colleague; how did you resolve it?
Tell me about a time you had a technical disagreement with a colleague; 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.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you prioritize your work when faced with multiple competing deadlines?
How do you prioritize your work when faced with multiple competing deadlines?
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.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a complex technical challenge you faced and the steps you took to overcome it.
Describe a complex technical challenge you faced and the steps you took to overcome 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.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Why are you interested in the intersection of creator economy and technology?
Why are you interested in the intersection of creator economy and technology?
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.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
What is your process for learning new technologies or frameworks when a project requires them?
What is your process for learning new technologies or frameworks when a project requires them?
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.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Tell me about a time you had a technical disagreement with a colleague; how did you resolve it?
- 02
How do you prioritize your work when faced with multiple competing deadlines?
- 03
Describe a complex technical challenge you faced and the steps you took to overcome it.
- 04
Why are you interested in the intersection of creator economy and technology?
How long does the entire process usually take?
The process is generally efficient, often spanning three to four weeks from the initial screen to the final offer decision.
Whalar Group Software Engineer candidate reports ↗Is the technical assignment live or take-home?
Whalar Group typically opts for a take-home assignment rather than live coding, as they believe it better reflects the actual work environment and reduces candidate stress.
Whalar Group Software Engineer candidate reports ↗Does the team expect me to know every language they use?
No. They prioritize your underlying engineering principles and your ability to learn. If you have a strong background in one major language, they are often flexible regarding the specific stack used in the home assignment.
Whalar Group Software Engineer candidate reports ↗What is the company culture like?
The culture is described as professional, friendly, and human-focused. The team values direct communication and avoids overly formal or "stiff" interview styles.
Whalar Group Software Engineer candidate reports ↗What topics does Whalar Group test in interviews?
Whalar Group interviews most often cover Professionalism, Communication, Take-Home Assignments, Docker, and Autonomous AI agents. The exact emphasis depends on the specific role you apply for.
Whalar Group Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Whalar Group Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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