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MKSK Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at MKSK is a pivotal role dedicated to building robust, scalable, and efficient web-based solutions. You will be responsible for the full development lifecycle, transforming complex requirements into high-quality code that powers the company’s digital infrastructure. Your work directly impacts how MKSK delivers value to its users, requiring a blend of technical precision and creative problem-solving. This role is not just about writing code; it is about architectural influence and strategic execution. Whether you are working on PHP-based legacy systems or modern Python web frameworks, you will contribute to critical projects that define the company's technical trajectory. You will collaborate with cross-functional teams to ensure that the solutions you build are performant, maintainable, and aligned with long-term business goals. 02 · Compensation

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

Focus areas

  • Software Engineering
  • Web Development
  • PHP

The MKSK interview process

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

Initial Screening

Initial vetting of candidates to assess basic qualifications and fit.

What to demonstrate

  • Initial vetting of candidates to assess basic qualifications and fit
  • Depth in Software Engineering

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 Discussions

Deep-dive technical discussions to evaluate candidates' technical depth.

What to demonstrate

  • Deep-dive technical discussions to evaluate candidates' technical depth
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: How do you handle dependency management and environment isolation in production deployments?
  • Answer aloud and timed: What are the most common security vulnerabilities in web applications, and how do you mitigate them?

Architectural Assessment

Candidates may undergo an architectural or practical coding assessment.

What to demonstrate

  • Candidates may undergo an architectural or practical coding assessment
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: Describe a time you had to debug a complex issue in a production environment.
  • Answer aloud and timed: How would you design a scalable API that handles millions of requests per day?

Where candidates lose points

Articulate your trade-offs

Whenever you propose a solution, explain why you chose it over the alternatives. This shows maturity and architectural awareness.

Ask clarifying questions

Don't rush into a solution. Clarify requirements and constraints to show you are thinking about the business impact.

Focus on the "why

Whether it's a coding challenge or a system design question, explain the underlying principles behind your choices.

Prepare your remote setup

Ensure your environment for the remote interview is stable and professional, as this is your primary workspace.

Questions & practice

How do you optimize database queries for high-traffic web applications?

Medium · Technical & Language Proficiency

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?

Can you explain the difference between synchronous and asynchronous processing in your preferred language?

Medium · Technical & Language Proficiency

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?

What are the most common security vulnerabilities in web applications, and how do you mitigate them?

Medium · Technical & Language Proficiency

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?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

  1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
  2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
  3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
  4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

  • A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
  • The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

  1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
  2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
  3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
  4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

  • The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
  • How do you verify the backfill actually finished, given rows are still being inserted while it runs?

How would you design a scalable API that handles millions of requests per day?

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

What factors do you consider when choosing between a relational and a NoSQL database for a new service?

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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 implement caching to improve the performance of a web application.

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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 high availability and fault tolerance in your system architecture?

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

  1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
  2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
  3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
  4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

  • The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
  • Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics MKSK candidates report.

Day 1 — Map the MKSK loop

  • Write out the reported sequence: Initial Screening, Technical Discussions, Architectural Assessment.
  • 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 Software Engineering

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

Deliverable: One timed worked example in Software Engineering.

Day 3 — Work Web Development

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

Deliverable: One timed worked example in Web Development.

Day 4 — Work PHP

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

Deliverable: One timed worked example in PHP.

Day 5 — Answer out loud: Technical & Language Proficiency

  • Answer aloud, timed: How do you optimize database queries for high-traffic web applications?
  • Answer aloud, timed: Can you explain the difference between synchronous and asynchronous processing in your preferred language?

Deliverable: Spoken answers to 2 reported Technical & Language Proficiency question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: How would you design a scalable API that handles millions of requests per day?
  • Answer aloud, timed: What factors do you consider when choosing between a relational and a NoSQL database for a new service?

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral & Problem Solving

  • Answer aloud, timed: Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder.
  • Answer aloud, timed: How do you handle disagreements within a development team regarding architectural choices?

Deliverable: Spoken answers to 2 reported Behavioral & Problem Solving question(s), under time.

Behavioural preparation

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

How do you handle dependency management and environment isolation in production deployments?

Medium · Technical & Language Proficiency

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 had to debug a complex issue in a production environment.

Medium · Technical & Language Proficiency

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 a time you had to explain a complex technical concept to a non-technical stakeholder.

Medium · Behavioral & Problem Solving

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 handle disagreements within a development team regarding architectural choices?

Medium · Behavioral & Problem Solving

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 your process for prioritizing tasks when faced with multiple urgent deadlines.

Medium · Behavioral & Problem Solving

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 stay updated with the latest industry trends and incorporate them into your work?

Medium · Behavioral & Problem Solving

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?