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Rana Software Engineer Interview Guide

Rana Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

The Software Engineer role at Rana is a critical function focused on building robust, scalable solutions that drive the company’s digital presence. Engineers in this position are expected to bridge the gap between complex backend logic and user-facing frontend experiences, ensuring that the software not only functions correctly but provides a seamless experience for the end user. You will contribute to the core technology stack, necessitating a strong grasp of both data structures and modern web development frameworks. This role is highly impactful, as your code directly influences how users interact with Rana platforms. Whether you are optimizing backend performance or refining frontend interactivity, your work is central to the company’s ability to solve real-world problems through efficient, maintainable software.

This guide is scoped to a Software Engineer candidate at Rana.

Focus areas

  • Java
  • Data Structures (DSA)
  • Algorithms (DSA)

The Rana interview process

Rana candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Online Technical Evaluation

Initial assessment to evaluate your technical skills and algorithmic thinking.

What to demonstrate

  • Initial assessment to evaluate your technical skills and algorithmic thinking
  • Depth in Java

How to prepare

  • Answer aloud and timed: Explain the time complexity of your solution for a given data structure problem.
  • Answer aloud and timed: How do you optimize a search algorithm for large datasets?

Human Resources Screening

Discussion with HR to assess cultural fit and gather background information.

What to demonstrate

  • Discussion with HR to assess cultural fit and gather background information
  • Depth in Java

How to prepare

  • Answer aloud and timed: Describe the core principles of object-oriented programming in the context of Java.
  • Answer aloud and timed: What is the difference between a stack and a queue in terms of memory management?

Technical Interview

In-person interview focusing on practical application of web technologies.

What to demonstrate

  • In-person interview focusing on practical application of web technologies
  • Depth in Java

How to prepare

  • Answer aloud and timed: How would you implement a linked list?
  • Answer aloud and timed: Explain how JavaScript objects and arrays function internally.

Where candidates lose points

Practice whiteboarding

Even in remote settings, be ready to explain your logic clearly as if you were drawing it out for a team member.

Review your own code

Be prepared to discuss any project on your resume in extreme detail, including why you chose specific libraries or architectures.

Be curious

Ask your interviewers questions about the team’s current challenges and the company’s technical roadmap.

Stay calm

The environment is meant to be professional and collaborative, not adversarial. Treat the interview as a conversation between peers.

Questions & practice

Explain the time complexity of your solution for a given data structure problem.

Medium · Technical Fundamentals and DSA

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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 optimize a search algorithm for large datasets?

Medium · Technical Fundamentals and DSA

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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 the difference between a stack and a queue in terms of memory management?

Medium · Technical Fundamentals and DSA

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

How would you implement a linked list?

Medium · Technical Fundamentals and DSA

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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 the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

  1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
  2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
  3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
  4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

  • A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
  • Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Describe the core principles of object-oriented programming in the context of Java.

Medium · Technical Fundamentals and DSA

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Explain how JavaScript objects and arrays function internally.

Medium · Web Development and Frameworks

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 frontend and backend state management?

Medium · Web Development and Frameworks

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 the lifecycle of a web request from the client to the server.

Medium · Web Development and Frameworks

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Why do you prefer specific frontend libraries over others for building responsive interfaces?

Medium · Web Development and Frameworks

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

  1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
  2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
  3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
  4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

  • Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
  • How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Rana candidates report.

Day 1 — Map the Rana loop

  • Write out the reported sequence: Online Technical Evaluation, Human Resources Screening, Technical Interview.
  • 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.

Day 2 — Work Java

  • Spend the session on Java, which Rana candidates report being tested on.
  • Write one worked example in Java and time yourself on it.

Deliverable: One timed worked example in Java.

Day 3 — Work Data Structures (DSA)

  • Spend the session on Data Structures (DSA), which Rana candidates report being tested on.
  • Write one worked example in Data Structures (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures (DSA).

Day 4 — Work Algorithms (DSA)

  • Spend the session on Algorithms (DSA), which Rana candidates report being tested on.
  • Write one worked example in Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Algorithms (DSA).

Day 5 — Answer out loud: Technical Fundamentals and DSA

  • Answer aloud, timed: Explain the time complexity of your solution for a given data structure problem.
  • Answer aloud, timed: How do you optimize a search algorithm for large datasets?

Deliverable: Spoken answers to 2 reported Technical Fundamentals and DSA question(s), under time.

Day 6 — Answer out loud: Web Development and Frameworks

  • Answer aloud, timed: Explain how JavaScript objects and arrays function internally.
  • Answer aloud, timed: What are the primary differences between frontend and backend state management?

Deliverable: Spoken answers to 2 reported Web Development and Frameworks question(s), under time.

Day 7 — Answer out loud: Behavioral and Motivation

  • Answer aloud, timed: Tell me about yourself and your journey into software development.
  • Answer aloud, timed: Why do you want to be a web developer at Rana?

Deliverable: Spoken answers to 2 reported Behavioral and Motivation question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How do you handle asynchronous operations in your preferred web framework?

Medium · Web Development and Frameworks

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Tell me about yourself and your journey into software development.

Medium · Behavioral and Motivation

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?

Why do you want to be a web developer at Rana?

Medium · Behavioral and Motivation

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Describe a time you faced a difficult technical challenge; how did you resolve it?

Medium · Behavioral and Motivation

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 keeps you motivated when working on complex, long-term projects?

Medium · Behavioral and Motivation

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

How do you approach learning new technologies when project requirements shift?

Medium · Behavioral and Motivation

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?