As a Software Engineer at GIVA, you will play a foundational role in building and scaling the technology that powers our platform. You will be tasked with solving complex technical challenges, ranging from optimizing data structures to designing robust, distributed systems that ensure high availability and performance. Your work directly impacts how our users interact with the platform, making your contributions central to the reliability and innovation of our product ecosystem. This role is designed for engineers who thrive on technical rigor and enjoy working in an environment where algorithmic efficiency and architectural integrity are highly valued. You will collaborate with cross-functional teams to translate business requirements into scalable code, ensuring that the systems you build can handle the demands of a growing user base. If you are passionate about writing clean, efficient, and maintainable software, you will find this position both challenging and rewarding.
Technical Screening
reportedInitial assessment to evaluate core engineering capabilities through hands-on coding.
What to demonstrate
- Initial assessment to evaluate core engineering capabilities through hands-on coding
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Implement an efficient solution for a given data structure manipulation.
- Answer aloud and timed: Solve a medium-difficulty algorithmic problem involving arrays or strings.
Technical Round 1
reportedFirst technical round focusing on algorithmic coding and problem-solving skills.
What to demonstrate
- First technical round focusing on algorithmic coding and problem-solving skills
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Optimize the time and space complexity of a provided code snippet.
- Answer aloud and timed: Demonstrate your approach to handling edge cases in recursive functions.
Technical Round 2
reportedSecond technical round involving high-level architectural discussions and critical thinking.
What to demonstrate
- Second technical round involving high-level architectural discussions and critical thinking
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Discuss the trade-offs between different data structures for a specific use case.
- Answer aloud and timed: Walk me through the most challenging technical project you have worked on.
PracHub editorial advice for the preparation topics above.
Focus on trade-offs
Whenever you propose a solution, mention the trade-offs (e.g., memory vs. speed). This shows architectural maturity.
Stay calm under pressure
If you get stuck on a coding problem, take a breath, explain your current thought process, and ask for a hint if needed. We want to see how you collaborate.
Review your resume
Be prepared to discuss every project you have listed in detail. Know the "why" behind your design choices for those projects.
Do not attempt to memorize solutions for common coding problems
Interviewers will often add variations to test your ability to adapt your logic in real-time.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement an efficient solution for a given data structure manipulation.
Implement an efficient solution for a given data structure manipulation.
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 medium-difficulty algorithmic problem involving arrays or strings.
Solve a medium-difficulty algorithmic problem involving arrays or strings.
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?
Optimize the time and space complexity of a provided code snippet.
Optimize the time and space complexity of a provided code snippet.
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?
Demonstrate your approach to handling edge cases in recursive functions.
Demonstrate your approach to handling edge cases in recursive functions.
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?
Discuss the trade-offs between different data structures for a specific use case.
Discuss the trade-offs between different data structures for a specific use case.
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?
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?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Walk me through the most challenging technical project you have worked on.
Walk me through the most challenging technical project you have worked on.
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?
Describe a situation where you had to learn a new technology under a tight deadline.
Describe a situation where you had to learn a new technology under a tight deadline.
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 was your specific contribution to the architecture of your previous project?
What was your specific contribution to the architecture of your previous project?
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 ensure the quality and maintainability of the code you write?
How do you ensure the quality and maintainability of the code you write?
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 design a system to handle high concurrency?
How would you design a system to handle high concurrency?
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 consistency and availability in a distributed system.
Discuss the trade-offs between consistency and availability 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?
Explain how you would handle data partitioning or sharding in a large-scale database.
Explain how you would handle data partitioning or sharding in a large-scale database.
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 monitoring and debugging a service in production?
How do you approach monitoring and debugging a service in production?
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 GIVA candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the GIVA loop
- Write out the reported sequence: Technical Screening, Technical Round 1, Technical Round 2.
- 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 GIVA 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 Problem Solving
- Spend the session on Problem Solving, which GIVA candidates report being tested on.
- Write one worked example in Problem Solving and time yourself on it.
Deliverable: One timed worked example in Problem Solving.
04Work Distributed Systems
- Spend the session on Distributed Systems, which GIVA candidates report being tested on.
- Write one worked example in Distributed Systems and time yourself on it.
Deliverable: One timed worked example in Distributed Systems.
05Answer out loud: Data Structures and Algorithms
- Answer aloud, timed: Implement an efficient solution for a given data structure manipulation.
- Answer aloud, timed: Solve a medium-difficulty algorithmic problem involving arrays or strings.
Deliverable: Spoken answers to 2 reported Data Structures and Algorithms question(s), under time.
06Answer out loud: Resume and Experience-Based Discussion
- Answer aloud, timed: Walk me through the most challenging technical project you have worked on.
- Answer aloud, timed: How did you resolve a conflict or technical disagreement within your team?
Deliverable: Spoken answers to 2 reported Resume and Experience-Based Discussion question(s), under time.
07Answer out loud: Distributed Systems
- Answer aloud, timed: How would you design a system to handle high concurrency?
- Answer aloud, timed: Discuss the trade-offs between consistency and availability in a distributed system.
Deliverable: Spoken answers to 2 reported Distributed Systems 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.
How did you resolve a conflict or technical disagreement within your team?
How did you resolve a conflict or technical disagreement within your 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?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
Tell callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
- 01
How did you resolve a conflict or technical disagreement within your team?
- 02
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
- 03
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
How much time should I dedicate to preparation?
Most successful candidates spend several weeks consistently practicing coding problems and reviewing system design fundamentals. Consistency is more important than cramming, so aim for daily, focused practice.
GIVA Software Engineer candidate reports ↗What is the most important factor in passing the interview?
The ability to communicate your thought process clearly is key. We want to see how you approach an ambiguous problem and how you pivot when you encounter roadblocks.
GIVA Software Engineer candidate reports ↗How long does the interview process typically take?
The process is designed to be efficient, typically moving from the first technical round to the final decision within a few weeks, depending on scheduling.
GIVA Software Engineer candidate reports ↗Does GIVA value specific programming languages?
We value strong engineering fundamentals over expertise in one specific language. If you can demonstrate mastery of core concepts, you will be well-prepared regardless of the language you choose for your coding interview.
GIVA Software Engineer candidate reports ↗What topics does GIVA test in interviews?
GIVA interviews most often cover Data Structures & Algorithms (DSA), Problem Solving, Distributed Systems, Algorithmic Reasoning, and Time Complexity Analysis. The exact emphasis depends on the specific role you apply for.
GIVA 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.
- 01GIVA 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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