The Software Engineer role at Logiology is a foundational position designed to bridge the gap between complex business requirements and scalable technical solutions. As a member of the engineering team, you will be responsible for developing, maintaining, and testing high-quality software that powers the company's core operations. Whether you are working on development, testing, or system support, your contribution directly impacts the reliability and efficiency of the platforms used by Logiology clients. This role is both challenging and rewarding, requiring a solid grasp of computer science fundamentals and a disciplined approach to problem-solving. You will work in a fast-paced environment where logic, code clarity, and architectural understanding are highly valued. Success in this position requires not only technical proficiency but also the ability to communicate your thought process clearly, as you will often collaborate with cross-functional teams to deliver robust software solutions.
Online Assessment
reportedInitial assessment to filter candidates based on fundamental logical and technical skills.
What to demonstrate
- Initial assessment to filter candidates based on fundamental logical and technical skills
- Depth in Object-Oriented Programming (OOP) concepts
How to prepare
- Answer aloud and timed: What are the four pillars of OOP, and can you provide examples for each?
- Answer aloud and timed: How do you differentiate between specific OOP concepts in a practical application?
Technical Rounds
reportedA series of technical interviews that may include phone screenings, face-to-face interviews, and practical machine tests.
What to demonstrate
- A series of technical interviews that may include phone screenings, face-to-face interviews, and practical machine tests
- Depth in Object-Oriented Programming (OOP) concepts
How to prepare
- Answer aloud and timed: Can you explain the basics of Java or C# as they relate to object-oriented design?
- Answer aloud and timed: How would you describe your project experience using the programming languages and tools you've selected?
PracHub editorial advice for the preparation topics above.
Prioritize Clarity
When writing code on paper or a whiteboard, use meaningful variable names. This shows you write code for others to read, not just for the machine to execute.
Prepare Your "Why
Be ready to explain why you chose a particular language or tool for your past projects.
Engage with the Interviewer
If you get stuck, talk through your thought process. Interviewers often provide hints if they see you are on the right track.
Review SQL Basics
Don't neglect database fundamentals; basic queries are a common part of the technical screening.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you write a program to generate a specific star or number pattern?
Can you write a program to generate a specific star or number pattern?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How would you approach solving a palindrome or Armstrong number problem?
How would you approach solving a palindrome or Armstrong number problem?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Can you explain your logic for manipulating arrays to solve a given task?
Can you explain your logic for manipulating arrays to solve a given task?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
What are the four pillars of OOP, and can you provide examples for each?
What are the four pillars of OOP, and can you provide examples for each?
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?
How do you differentiate between specific OOP concepts in a practical application?
How do you differentiate between specific OOP concepts in a practical application?
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?
Can you explain the basics of Java or C# as they relate to object-oriented design?
Can you explain the basics of Java or C# as they relate to object-oriented design?
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?
How would you describe your project experience using the programming languages and tools you've selected?
How would you describe your project experience using the programming languages and tools you've selected?
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?
What are the primary differences between Black Box and White Box testing?
What are the primary differences between Black Box and White Box testing?
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?
Can you explain the various levels of testing and when each is applied?
Can you explain the various levels of testing and when each is applied?
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?
What distinguishes SDLC from STLC in a professional project environment?
What distinguishes SDLC from STLC in a professional project environment?
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?
How do you differentiate between Smoke testing and Sanity testing?
How do you differentiate between Smoke testing and Sanity testing?
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?
What is your process for debugging a coding problem when the output is not as expected?
What is your process for debugging a coding problem when the output is not as expected?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Logiology candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Logiology loop
- Write out the reported sequence: Online Assessment, Technical Rounds.
- 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 2 reported rounds, with the weakest marked.
02Work Object-Oriented Programming (OOP) concepts
- Spend the session on Object-Oriented Programming (OOP) concepts, which Logiology candidates report being tested on.
- Write one worked example in Object-Oriented Programming (OOP) concepts and time yourself on it.
Deliverable: One timed worked example in Object-Oriented Programming (OOP) concepts.
03Work OOP 4 pillars (Abstraction, Encapsulation, Inheritance, Polymorphism)
- Spend the session on OOP 4 pillars (Abstraction, Encapsulation, Inheritance, Polymorphism), which Logiology candidates report being tested on.
- Write one worked example in OOP 4 pillars (Abstraction, Encapsulation, Inheritance, Polymorphism) and time yourself on it.
Deliverable: One timed worked example in OOP 4 pillars (Abstraction, Encapsulation, Inheritance, Polymorphism).
04Work Pattern printing / pattern programs
- Spend the session on Pattern printing / pattern programs, which Logiology candidates report being tested on.
- Write one worked example in Pattern printing / pattern programs and time yourself on it.
Deliverable: One timed worked example in Pattern printing / pattern programs.
05Answer out loud: Programming Fundamentals & OOP
- Answer aloud, timed: What are the four pillars of OOP, and can you provide examples for each?
- Answer aloud, timed: How do you differentiate between specific OOP concepts in a practical application?
Deliverable: Spoken answers to 2 reported Programming Fundamentals & OOP question(s), under time.
06Answer out loud: Algorithmic & Logical Coding
- Answer aloud, timed: Can you write a program to generate a specific star or number pattern?
- Answer aloud, timed: How would you approach solving a palindrome or Armstrong number problem?
Deliverable: Spoken answers to 2 reported Algorithmic & Logical Coding question(s), under time.
07Answer out loud: Testing & Quality Assurance
- Answer aloud, timed: What are the primary differences between Black Box and White Box testing?
- Answer aloud, timed: Can you explain the various levels of testing and when each is applied?
Deliverable: Spoken answers to 2 reported Testing & Quality Assurance question(s), under time.
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.
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?
Reverse your own decision and price the reversal
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
Approach
- State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
- Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
- Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
- Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.
Follow-up
- What in that decision was irreversible, and did you know it was irreversible when you made it?
- How did you tell the people who had already built on top of the original decision?
- 01
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.
- 02
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.
- 03
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
How much time should I spend preparing?
Given the focus on fundamentals, we recommend dedicating at least 2–3 weeks of focused practice on coding patterns and OOP concepts. Consistency in solving 1–2 coding problems daily is more effective than last-minute cramming.
Logiology Software Engineer candidate reports ↗Does Logiology prioritize experience over fresh graduates?
While the company values experienced talent, they regularly hire and train junior engineers. The evaluation process is designed to find individuals with the right potential, regardless of their professional tenure.
Logiology Software Engineer candidate reports ↗How should I handle the machine test?
Focus on getting the logic correct first. If you have time, focus on syntax, but interviewers are primarily looking for your ability to solve the problem presented.
Logiology Software Engineer candidate reports ↗Is the interview process mostly remote or in-person?
The process typically involves a mix, starting with online assessments or phone calls and culminating in face-to-face interactions.
Logiology Software Engineer candidate reports ↗What topics does Logiology test in interviews?
Logiology interviews most often cover Object-Oriented Programming (OOP) concepts, OOP 4 pillars (Abstraction, Encapsulation, Inheritance, Polymorphism), Pattern printing / pattern programs, Levels of testing, and Black box testing. The exact emphasis depends on the specific role you apply for.
Logiology 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.
- 01Logiology 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
Explore related engineering environments and compare the preparation focus.
Confluent Software Engineer Interview Guide
Concurrency exercises for Confluent candidates: check-then-act races, deadlock from inconsistent lock ordering, and why two individually atomic operations do not compose into an atomic update.
GitHub Software Engineer Interview Questions & Guide 2026
Testing exercises for GitHub software engineer prep: choosing boundary cases over repetition, isolating tests from shared clock and ordering, and treating an intermittent failure as a defect rather than noise.
Zulily Software Engineer Interview Guide
What Zulily asks Software Engineer candidates, from reported rounds and questions.
Zoom Video Communications Software Engineer Interview Guide
What Zoom Video Communications asks Software Engineer candidates, from reported rounds and questions.