As a Software Engineer at Roima Intelligence, you are at the core of delivering high-impact industrial software solutions. You will be responsible for designing, developing, and maintaining complex systems that drive operational efficiency for our clients. Your work directly influences how businesses manage data, optimize their processes, and leverage technology to solve real-world industrial challenges. This role requires a blend of rigorous technical foundation and practical problem-solving. You will contribute to the full software development lifecycle, from translating business requirements into robust database schemas to implementing scalable code. It is a position that demands both individual precision and the ability to work within a team environment to deliver high-quality, reliable software in a fast-paced, results-oriented culture. ##### Tip Prepare to demonstrate your technical depth, as Roima Intelligence values candidates who can bridge the gap between theoretical computer science knowledge and practical, optimized application.
Written Evaluation
reportedCandidates begin with a written evaluation to assess their technical proficiency.
What to demonstrate
- Candidates begin with a written evaluation to assess their technical proficiency
- Depth in Data Structures and Algorithms (DSA)
How to prepare
- Answer aloud and timed: How do you approach the normalization of a database schema for a complex system, such as a sports store or hospital management software?
- Answer aloud and timed: Can you explain the difference between various types of joins and when to use each in a real-world query?
Technical Discussions
reportedFollowing the written evaluation, candidates engage in technical discussions to further evaluate their skills.
What to demonstrate
- Following the written evaluation, candidates engage in technical discussions to further evaluate their skills
- Depth in Data Structures and Algorithms (DSA)
How to prepare
- Answer aloud and timed: How do you handle database performance issues caused by unnecessary or inefficient queries?
- Answer aloud and timed: What are the ACID properties, and why are they critical in transactional database design?
Behavioral Discussions
reportedCandidates participate in behavioral discussions to assess their alignment with the company's operational standards.
What to demonstrate
- Candidates participate in behavioral discussions to assess their alignment with the company's operational standards
- Depth in Data Structures and 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 behavioral discussions above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Structure your thoughts
When asked a question, take a moment to outline your approach before diving into the solution.
Be prepared for pen and paper
Do not rely solely on IDEs; practice writing code manually to ensure your syntax and logic are clear without automated help.
Maintain professionalism
Regardless of the intensity of the interview, maintain a respectful and calm demeanor, as this reflects your ability to work in a professional team.
Know your resume
Be prepared to discuss every technical project you have listed in detail, especially regarding the database design and the specific challenges you faced.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you optimize code for time and space complexity in a resource-constrained environment?
How do you optimize code for time and space complexity in a resource-constrained environment?
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 the process of finding the longest common substring without repeating characters?
Can you explain the process of finding the longest common substring without repeating characters?
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 your approach to identifying the smallest missing integer in a given array?
What is your approach to identifying the smallest missing integer in a given array?
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 how you would implement event log tracking for users using efficient data structures.
Describe how you would implement event log tracking for users using efficient data structures.
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 do you apply Object-Oriented Programming (OOP) principles to structure a large-scale project?
How do you apply Object-Oriented Programming (OOP) principles to structure a large-scale project?
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 do you approach the normalization of a database schema for a complex system, such as a sports store or hos
How do you approach the normalization of a database schema for a complex system, such as a sports store or hospital management software?
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?
Can you explain the difference between various types of joins and when to use each in a real-world query?
Can you explain the difference between various types of joins and when to use each in a real-world 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?
Can you write a SQL query to retrieve specific data from a multi-table database with complex conditions?
Can you write a SQL query to retrieve specific data from a multi-table database with complex conditions?
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 ACID properties, and why are they critical in transactional database design?
What are the ACID properties, and why are they critical in transactional database 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?
Convert the listing endpoint from offset pages to stable cursors
GET /v1/resources returns a tenant's resources newest-updated first, today with page and per_page, backed by index (tenant_id, status, updated_at DESC, resource_id DESC). Callers are a browser feed and a nightly sync job that walks every page. Users report items appearing twice or vanishing between pages, and page 400 is slow. Design the cursor contract: what the cursor contains and how it is encoded, the exact WHERE and ORDER BY, what happens when a row's updated_at changes mid-walk, how a client detects the end, and what the sync job does when a cursor is rejected.
Approach
- Separate the two defects, because they have different fixes. Cost: OFFSET n makes the engine produce and discard n rows, so price grows with page depth rather than page size and page 400 pays for 400 pages of work. Correctness: while the set shifts, rows cross the offset boundary and are skipped or repeated, and nothing in the response lets the client detect it.
- Write the seek: WHERE tenant_id = $1 AND status = $2 AND (updated_at, resource_id) < ($k, $id) ORDER BY updated_at DESC, resource_id DESC LIMIT n. The tie-break is not decoration - updated_at is not unique, and two rows sharing a timestamp across a page boundary reintroduce exactly the skip this was adopted to remove. PostgreSQL seeks the composite index on the row-value comparison directly; on an engine that does not optimise a row constructor, expand it into the equivalent OR form or the plan quietly degrades to a scan.
- Encode the cursor as an opaque token carrying the sort key, the id, and a fingerprint of the filter and sort order, signed or at minimum validated. A cursor replayed against a different sort or filter must be a 400 with its own code, not a silently wrong page - the sync job cannot notice the difference otherwise.
- State the guarantee precisely rather than generously. Keyset is stable against inserts and deletes elsewhere in the set, because the position is a value and not a count. It is not a snapshot: updated_at is mutable, so a row that is edited during the walk re-sorts and may be seen twice or not at all. If the sync job needs exactly-once coverage, order on an immutable key such as (created_at, resource_id), or walk resource_revision by revision_id and treat updated_at as data.
Follow-up
- The client wants a total count and the ability to jump to page 400. What can you honestly offer instead, and what does each option cost?
- How do you paginate backwards, and what does that require of the index?
Exports duplicate a row range about once a week
Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.
Approach
- Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
- Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
- Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
- Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.
Follow-up
- The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
- You now count takeovers. What alert fires on that counter, and at what threshold?
Built from the rounds and topics Roima Intelligence candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Roima Intelligence loop
- Write out the reported sequence: Written Evaluation, Technical Discussions, Behavioral Discussions.
- 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 and Algorithms (DSA)
- Spend the session on Data Structures and Algorithms (DSA), which Roima Intelligence candidates report being tested on.
- Write one worked example in Data Structures and Algorithms (DSA) and time yourself on it.
Deliverable: One timed worked example in Data Structures and Algorithms (DSA).
03Work SQL
- Spend the session on SQL, which Roima Intelligence candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
04Work Aptitude/Reasoning
- Spend the session on Aptitude/Reasoning, which Roima Intelligence candidates report being tested on.
- Write one worked example in Aptitude/Reasoning and time yourself on it.
Deliverable: One timed worked example in Aptitude/Reasoning.
05Answer out loud: Technical Foundations (DBMS & SQL)
- Answer aloud, timed: How do you approach the normalization of a database schema for a complex system, such as a sports store or hospital management software?
- Answer aloud, timed: Can you explain the difference between various types of joins and when to use each in a real-world query?
Deliverable: Spoken answers to 2 reported Technical Foundations (DBMS & SQL) question(s), under time.
06Answer out loud: Programming & Algorithms
- Answer aloud, timed: How do you optimize code for time and space complexity in a resource-constrained environment?
- Answer aloud, timed: Can you explain the process of finding the longest common substring without repeating characters?
Deliverable: Spoken answers to 2 reported Programming & Algorithms question(s), under time.
07Dry run for Roima Intelligence
- 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.
How do you handle database performance issues caused by unnecessary or inefficient queries?
How do you handle database performance issues caused by unnecessary or inefficient queries?
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?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
Unblock an engineer without taking the keyboard
A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.
Approach
- Ask before diagnosing, and ask for things answerable from data they already have: the attempt count on the job rows that produced duplicates, the handler's observed duration against its lease expiry, and whether the duplicate rows share a natural key that a unique constraint could have caught.
- Teach the shape rather than the answer. A lease cannot distinguish a dead worker from a slow one, so a handler that outruns its lease is running twice by design, and deploys deliver the other half by killing handlers mid-run on every rollout. Both of their candidate theories produce identical duplicate rows, which is why the evidence has to come from timings rather than from argument.
- Hand over a checklist they execute: a natural key on every write the handler performs so the second copy collides rather than appends, the record of intent written before any external effect, a lease heartbeat while running, and the metric that shows it working.
- Keep ownership with them deliberately. Pair on the first write, then step back; if you finish it yourself you have closed one ticket and left the same person stuck on the next redelivery.
Follow-up
- How would you distinguish a genuine double-delivery from a lease expiry using only the data already stored?
- Their handler calls an external endpoint before recording that it did. What do you tell them to change first?
- 01
How do you handle database performance issues caused by unnecessary or inefficient queries?
- 02
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
- 03
A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.
How difficult are the technical rounds?
The difficulty is generally considered moderate to high. The focus is on your ability to apply core concepts to real-world scenarios, so ensure your fundamentals in SQL and OOP are rock-solid.
Roima Intelligence Software Engineer candidate reports ↗Is there a bond requirement for new hires?
Yes, specific terms regarding employment bonds and performance deposits are typically discussed during the HR round. Ensure you seek full clarity on these policies during your interview.
Roima Intelligence Software Engineer candidate reports ↗What is the best way to prepare for the written test?
Practice coding on paper to get comfortable with the format. Focus on solving 20-30 medium-level coding challenges and SQL query exercises daily in the lead-up to your interview.
Roima Intelligence Software Engineer candidate reports ↗What topics does Roima Intelligence test in interviews?
Roima Intelligence interviews most often cover Object-Oriented Programming (OOP), Data Structures and Algorithms (DSA), DBMS (Database Management Systems), SQL, and SQL (Structured Query Language). The exact emphasis depends on the specific role you apply for.
Roima Intelligence 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.
- 01Roima Intelligence 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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