As a Software Engineer at Licious, you will play a pivotal role in shaping the software solutions that drive our business and enhance user experiences. This position is crucial for the development and maintenance of our innovative products, which cater to the ever-evolving needs of our customers. You will work within dynamic teams, tackling complex challenges that involve designing scalable systems, ensuring high performance, and integrating user feedback into our development cycles. This role not only demands strong technical skills but also the ability to collaborate effectively with cross-functional teams, including product management and operations. You will have the opportunity to contribute to projects that have a tangible impact on our customers, from optimizing our inventory systems to improving the user interface of our mobile applications. At Licious, your work as a Software Engineer will directly influence the quality of our services and the satisfaction of our users, making it an exciting and strategically important position.
Initial Screening
reportedAn initial screening phase, often involving an online coding test.
What to demonstrate
- An initial screening phase, often involving an online coding test
- Depth in Data Structures & Algorithms (DSA)
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 Interviews
reportedSeveral technical interviews that may cover algorithms, system design, and behavior.
What to demonstrate
- Several technical interviews that may cover algorithms, system design, and behavior
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: What are the main differences between REST and GraphQL?
- Answer aloud and timed: Can you describe the Observer Design Pattern and provide a use case?
Final HR Round
reportedDiscussion about your fit within the company culture and potential compensation.
What to demonstrate
- Discussion about your fit within the company culture and potential compensation
- Depth in Data Structures & Algorithms (DSA)
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 final hr round above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Practice Coding Regularly
Engaging with platforms like LeetCode or HackerRank can sharpen your coding skills and prepare you for technical assessments.
Understand System Design
Familiarize yourself with design principles and be ready to discuss your design choices in interviews.
Communicate Your Thought Process
During technical interviews, articulate your reasoning clearly to help interviewers understand your approach.
Be Prepared for Behavioral Questions
Reflect on past experiences that highlight your problem-solving skills and teamwork capabilities.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the concept of a hash table and its applications.
Explain the concept of a hash table and its applications.
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 implement a binary search tree? Discuss its time complexity.
How would you implement a binary search tree? Discuss its time complexity.
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?
What is Big O notation, and why is it important?
What is Big O notation, and why is it important?
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?
Write a function to detect a cycle in a linked list.
Write a function to detect a cycle in a linked list.
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?
Solve a problem involving dynamic programming, such as the Knapsack problem.
Solve a problem involving dynamic programming, such as the Knapsack 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?
Given an array, find the maximum product of two integers.
Given an array, find the maximum product of two integers.
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?
Describe your process for optimizing a slow-running SQL query.
Describe your process for optimizing a slow-running SQL query.
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?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
What are the main differences between REST and GraphQL?
What are the main differences between REST and GraphQL?
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?
Can you describe the Observer Design Pattern and provide a use case?
Can you describe the Observer Design Pattern and provide a use case?
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?
Design a URL shortener service. What considerations would you take into account?
Design a URL shortener service. What considerations would you take into account?
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 would you approach building a real-time chat application? Outline the architecture.
How would you approach building a real-time chat application? Outline the architecture.
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?
Discuss the trade-offs between a monolithic and a microservices architecture.
Discuss the trade-offs between a monolithic and a microservices architecture.
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 would you ensure data consistency in a distributed system?
How would you ensure data consistency in a distributed system?
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?
Design a doctor appointment scheduling system and explain your design choices.
Design a doctor appointment scheduling system and explain your design choices.
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 would you handle a situation where a critical bug was found just before a product launch?
How would you handle a situation where a critical bug was found just before a product launch?
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?
Given a scenario where user engagement is declining, how would you investigate and address the issue?
Given a scenario where user engagement is declining, how would you investigate and address the issue?
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?
Implement an LRU Cache. Discuss your design and the data structures you would use.
Implement an LRU Cache. Discuss your design and the data structures you would use.
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 would you approach debugging a complex application?
How would you approach debugging a complex application?
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 Licious candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Licious loop
- Write out the reported sequence: Initial Screening, Technical Interviews, Final HR Round.
- 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 3 reported rounds, with the weakest marked.
02Work Data Structures & Algorithms (DSA)
- Spend the session on Data Structures & Algorithms (DSA), which Licious candidates report being tested on.
- Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.
Deliverable: One timed worked example in Data Structures & Algorithms (DSA).
03Work Low-Level Design (LLD)
- Spend the session on Low-Level Design (LLD), which Licious candidates report being tested on.
- Write one worked example in Low-Level Design (LLD) and time yourself on it.
Deliverable: One timed worked example in Low-Level Design (LLD).
04Work Cache Systems (LRU/LFU)
- Spend the session on Cache Systems (LRU/LFU), which Licious candidates report being tested on.
- Write one worked example in Cache Systems (LRU/LFU) and time yourself on it.
Deliverable: One timed worked example in Cache Systems (LRU/LFU).
05Answer out loud: Technical / Domain Questions
- Answer aloud, timed: Explain the concept of a hash table and its applications.
- Answer aloud, timed: How would you implement a binary search tree? Discuss its time complexity.
Deliverable: Spoken answers to 2 reported Technical / Domain Questions question(s), under time.
06Answer out loud: System Design / Architecture
- Answer aloud, timed: Design a URL shortener service. What considerations would you take into account?
- Answer aloud, timed: How would you approach building a real-time chat application? Outline the architecture.
Deliverable: Spoken answers to 2 reported System Design / Architecture question(s), under time.
07Answer out loud: Behavioral / Leadership
- Answer aloud, timed: Describe a time when you faced a significant challenge in a project. How did you handle it?
- Answer aloud, timed: How do you prioritize tasks when managing multiple projects?
Deliverable: Spoken answers to 2 reported Behavioral / Leadership 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.
Describe a time when you faced a significant challenge in a project. How did you handle it?
Describe a time when you faced a significant challenge in a project. How did you handle 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 tasks when managing multiple projects?
How do you prioritize tasks when managing multiple projects?
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?
Can you give an example of how you worked effectively with a team?
Can you give an example of how you worked effectively with a team?
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 motivates you as an engineer, and how do you keep yourself updated with technology trends?
What motivates you as an engineer, and how do you keep yourself updated with technology trends?
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 feedback and criticism?
How do you handle feedback and criticism?
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 a time when you faced a significant challenge in a project. How did you handle it?
- 02
How do you prioritize tasks when managing multiple projects?
- 03
Can you give an example of how you worked effectively with a team?
- 04
What motivates you as an engineer, and how do you keep yourself updated with technology trends?
How difficult is the interview process?
The interview process at Licious is considered average in difficulty, with a mix of technical and behavioral questions. Candidates should prepare by practicing coding and system design problems.
Licious Software Engineer candidate reports ↗What differentiates successful candidates?
Successful candidates demonstrate strong technical expertise, effective communication skills, and a good cultural fit with the company's values. They also show a passion for learning and growth.
Licious Software Engineer candidate reports ↗What is the culture like at Licious?
Licious fosters a collaborative and innovative culture where teamwork and integrity are valued. Employees are encouraged to share ideas and contribute to the company's success.
Licious Software Engineer candidate reports ↗What is the typical timeline from initial screen to offer?
The interview process can take anywhere from two to four weeks, depending on scheduling and the number of candidates. Candidates will typically receive feedback after each round.
Licious Software Engineer candidate reports ↗Are there remote work opportunities?
While the primary work environment is office-based, Licious has embraced flexible work arrangements, including remote work options, depending on the role and team needs.
Licious Software Engineer candidate reports ↗What topics does Licious test in interviews?
Licious interviews most often cover Problem Solving, Data Structures & Algorithms (DSA), Low-Level Design (LLD), High-Level Design (HLD), and Stakeholder Management. The exact emphasis depends on the specific role you apply for.
Licious 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.
- 01Licious 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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