A Software Engineer at One call is a critical architect of our technical ecosystem, responsible for building scalable, reliable, and high-impact software solutions. You will work at the intersection of complex business logic and modern engineering practices, translating high-level product requirements into functional, maintainable code. Your contributions directly influence our ability to deliver value to our users, ensuring that our platforms remain robust and performant. This role is not merely about writing code; it is about taking ownership of the development lifecycle. You will participate in technical design, implement automated testing, and contribute to our operational health through observability and CI/CD excellence. We look for engineers who are eager to leverage AI-led development tools to accelerate delivery and who thrive in a collaborative environment where mentorship and continuous improvement are prioritized.
Initial Screening
reportedDiscuss your background and interest in the role with a recruiter or hiring manager.
What to demonstrate
- Discuss your background and interest in the role with a recruiter or hiring manager
- Depth in .NET Frameworks
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.
Technical Deep-Dives
reportedEngage in a series of technical assessments including architectural design discussions and coding evaluations.
What to demonstrate
- Engage in a series of technical assessments including architectural design discussions and coding evaluations
- Depth in .NET Frameworks
How to prepare
- Answer aloud and timed: Describe your experience with.NET frameworks and how you handle dependency injection.
- Answer aloud and timed: What are the trade-offs between SQL and NoSQL databases for high-scale applications?
Behavioral Interviews
reportedParticipate in interviews with senior engineers or managers to discuss past projects and decisions.
What to demonstrate
- Participate in interviews with senior engineers or managers to discuss past projects and decisions
- Depth in .NET Frameworks
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral interviews above and write down what you would ask to confirm before it.
Final Evaluation
reportedConclude the interview process with a comprehensive assessment of your fit for the team and role.
What to demonstrate
- Conclude the interview process with a comprehensive assessment of your fit for the team and role
- Depth in .NET Frameworks
How to prepare
- Answer aloud and timed: How do you handle high-pressure situations or tight deadlines when building features?
- Answer aloud and timed: Describe a project where you took full ownership of a feature from design to deployment.
PracHub editorial advice for the preparation topics above.
Own your narrative
Be prepared to discuss your resume in detail. Focus on the impact of your work rather than just the tasks you performed.
Ask meaningful questions
Use your time with interviewers to ask about the team’s current challenges or how they balance technical debt with new feature development.
Focus on the "Why
In technical discussions, explain the rationale behind your decisions. We value engineers who understand the trade-offs of their choices.
Be transparent about your experience
If you are unfamiliar with a specific tool, show how you would go about learning it. We value high-learning agility.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
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?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Explain the difference between a trigger and a stored procedure in SQL.
Explain the difference between a trigger and a stored procedure in SQL.
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?
How do you optimize a query that is performing poorly in a production environment?
How do you optimize a query that is performing poorly in a production environment?
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?
What are the trade-offs between SQL and NoSQL databases for high-scale applications?
What are the trade-offs between SQL and NoSQL databases for high-scale applications?
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?
How do you ensure your code is testable and maintainable within a CI/CD pipeline?
How do you ensure your code is testable and maintainable within a CI/CD pipeline?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Relay committed events to the log without gaps or reordering
outbox_event rows are written in the same transaction as the state change and carry aggregate_type, aggregate_id, aggregate_version, payload and status, with a partial index on (created_at, event_id) WHERE status = 'pending'. The relay publishes about 4k events/second to a partitioned append-only log keyed by aggregate_id, with one leader per partition range holding a lease. Consumers must never miss an event; they may see one twice. Design the claim-publish-mark loop, and state exactly what consumers observe when a leader's lease expires while it is mid-batch.
Approach
- Claim with SELECT ... WHERE status='pending' ORDER BY created_at, event_id LIMIT $batch FOR UPDATE SKIP LOCKED inside a transaction. SKIP LOCKED lets several relay workers share a range without serialising on each other's rows, and the partial index keeps the claim proportional to the backlog rather than to a table that is overwhelmingly published rows. At 4k/second a batch of 500 is eight claims per second, each an index scan of 500 entries.
- Publish before marking, never the reverse, and say why it is a choice. Marking first loses the event outright if the process dies in the gap, and the loss is silent - nothing remains to retry, and it surfaces later as a projection missing a row. Publishing first can repeat the event, and repetition is what every consumer is already built to survive. That single ordering is the whole at-least-once guarantee.
- Preserve the only ordering on offer. Partition by aggregate_id and never publish two events for one aggregate concurrently: claim in (created_at, event_id) order and publish sequentially within an aggregate, or hash aggregate_id to a worker slot. Order across aggregates is not available at any price here, which is why the event carries aggregate_version and the full fact rather than a delta - a consumer can then discard what it has already applied without coordinating with anyone.
- State the failover behaviour precisely, because it is the consistency-versus-availability decision in this design. A lease expires because the holder is slow, and no mechanism distinguishes that from dead, so for the length of the lease window two leaders can publish the same claimed batch. The system accepts duplicates to avoid stalling publication for every aggregate in the range whenever one worker pauses. Consumers deduplicate on (aggregate_id, aggregate_version) and drop anything at or below what they have applied.
Follow-up
- The relay is down 40 minutes and 9.6 million rows are pending. What does catch-up do to the primary, and what changes in the claim loop to survive it?
- A consumer insists it never received an event. Which single query settles whether the relay lost it, and what does each answer look like?
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 One call candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the One call loop
- Write out the reported sequence: Initial Screening, Technical Deep-Dives, Behavioral Interviews, Final Evaluation.
- 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 .NET Frameworks
- Spend the session on .NET Frameworks, which One call candidates report being tested on.
- Write one worked example in .NET Frameworks and time yourself on it.
Deliverable: One timed worked example in .NET Frameworks.
03Work Backend Development
- Spend the session on Backend Development, which One call candidates report being tested on.
- Write one worked example in Backend Development and time yourself on it.
Deliverable: One timed worked example in Backend Development.
04Work Cloud Computing (AWS/Azure/GCP)
- Spend the session on Cloud Computing (AWS/Azure/GCP), which One call candidates report being tested on.
- Write one worked example in Cloud Computing (AWS/Azure/GCP) and time yourself on it.
Deliverable: One timed worked example in Cloud Computing (AWS/Azure/GCP).
05Answer out loud: Technical & Domain Proficiency
- Answer aloud, timed: Explain the difference between a trigger and a stored procedure in SQL.
- Answer aloud, timed: How do you optimize a query that is performing poorly in a production environment?
Deliverable: Spoken answers to 2 reported Technical & Domain Proficiency question(s), under time.
06Answer out loud: Behavioral & Cultural Alignment
- Answer aloud, timed: Tell me about a time you had to resolve a technical disagreement with a peer or stakeholder.
- Answer aloud, timed: How do you handle high-pressure situations or tight deadlines when building features?
Deliverable: Spoken answers to 2 reported Behavioral & Cultural Alignment question(s), under time.
07Dry run for One call
- 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.
Describe your experience with.NET frameworks and how you handle dependency injection.
Describe your experience with.NET frameworks and how you handle dependency injection.
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?
Tell me about a time you had to resolve a technical disagreement with a peer or stakeholder.
Tell me about a time you had to resolve a technical disagreement with a peer or stakeholder.
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 handle high-pressure situations or tight deadlines when building features?
How do you handle high-pressure situations or tight deadlines when building features?
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 project where you took full ownership of a feature from design to deployment.
Describe a project where you took full ownership of a feature from design to deployment.
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 approach giving and receiving critical feedback during code reviews?
How do you approach giving and receiving critical feedback during code reviews?
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 do you do when you encounter a legacy system that requires significant refactoring?
What do you do when you encounter a legacy system that requires significant refactoring?
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
Describe your experience with.NET frameworks and how you handle dependency injection.
- 02
Tell me about a time you had to resolve a technical disagreement with a peer or stakeholder.
- 03
How do you handle high-pressure situations or tight deadlines when building features?
- 04
Describe a project where you took full ownership of a feature from design to deployment.
How should I prepare for the technical rounds?
Focus on your core competencies in.NET and database design. Be ready to explain your past decisions, including why you chose specific technologies and how you handled trade-offs.
One call Software Engineer candidate reports ↗What is the company culture like?
We value ownership, speed, and deep care for our product and team. Successful candidates are those who are proactive, communicate clearly, and are genuinely excited to solve complex problems as a team.
One call Software Engineer candidate reports ↗Is there an on-call requirement?
Yes, as part of our commitment to operational excellence, engineers participate in on-call rotations. This ensures that we take ownership of our services from development through to production support.
One call Software Engineer candidate reports ↗What topics does One call test in interviews?
One call interviews most often cover SQL, Stakeholder Management, Analytical Problem Solving, Enterprise Sales (B2B), and Financial Analysis (FP&A). The exact emphasis depends on the specific role you apply for.
One call 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.
- 01One call 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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