Kantar Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Kantar, you are at the intersection of high-scale data analytics and actionable business intelligence. You will be responsible for building, maintaining, and scaling the robust platforms that help the world’s leading brands understand consumer behavior. Your work directly influences how clients interpret complex market trends, making your contributions central to the company’s mission of delivering evidence-based insights. The environment is fast-paced and intellectually demanding, requiring a balance of rigorous technical execution and collaborative problem-solving. You will work within diverse, cross-functional teams to tackle challenges ranging from data processing and backend architecture to intuitive frontend development.

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

Focus areas

• Object-Oriented Programming (OOP)
• Databases (DBMS)
• SQL (Querying)

The Kantar interview process

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

Initial Screening Call

A call with a recruiter to assess your fit for the role.

What to demonstrate

• A call with a recruiter to assess your fit for the role
• Depth in Object-Oriented Programming (OOP)

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 Rounds

One or more rounds focusing on technical skills through coding exercises and deep dives.

What to demonstrate

• One or more rounds focusing on technical skills through coding exercises and deep dives
• Depth in Object-Oriented Programming (OOP)

How to prepare

• Answer aloud and timed: Describe the difference between various SQL Joins and when to use them.
• Answer aloud and timed: What are the key differences between DDL and DML commands in SQL?

Behavioral Interview

A discussion to evaluate cultural fit and behavioral aspects of your experience.

What to demonstrate

• A discussion to evaluate cultural fit and behavioral aspects of your experience
• Depth in Object-Oriented Programming (OOP)

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 interview above and write down what you would ask to confirm before it.

Where candidates lose points

Master the Fundamentals

Do not neglect basics like OOPs and DBMS. These are frequently tested and serve as a baseline for your technical credibility.

Practice Live Coding

Whether it is an online platform or a whiteboard session, practice explaining your thought process while you write code.

Know Your Resume

Be prepared to discuss every project you have listed in detail. Interviewers often use your resume as a starting point for technical deep dives.

Prepare Questions

Always have insightful questions ready for your interviewers. This demonstrates your engagement and interest in the specific challenges of the team you are interviewing for.

Be Transparent

If you don't know an answer, it is better to explain your reasoning and how you would go about finding the answer rather than guessing.

Questions & practice

Explain the difference between Call by Value and Call by Reference.

Medium · Technical Fundamentals

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?

Write a function to perform basic operations on an Array.

Medium · Coding and Problem Solving

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?

Solve a medium-level LeetCode style problem involving strings or greedy algorithms.

Medium · Coding and Problem Solving

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?

Given a dataset, how would you identify seasonal trends or outliers?

Medium · Coding and Problem Solving

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?

Explain your approach to solving a Dynamic Programming problem.

Medium · Coding and Problem Solving

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?

Walk us through a piece of code you have written and explain how you would improve it.

Medium · Coding and Problem Solving

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?

Describe the difference between various SQL Joins and when to use them.

Medium · Technical Fundamentals

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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 key differences between DDL and DML commands in SQL?

Medium · Technical Fundamentals

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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 how you would optimize a slow-running database query?

Medium · Technical Fundamentals

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

How do you manage Object-Oriented Programming (OOPs) principles in your code?

Medium · Technical Fundamentals

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?

Keep one tenant's bulk export from starving projection updates

Medium · backpressure · fair scheduling · head-of-line blocking

The worker fleet runs about 600 jobs/second out of job_run, claimed under a lease and heartbeated while running. Most handlers finish under 200 ms; exports run for minutes. One tenant enqueues 50,000 exports. Design the scheduling so that tenant's exports slow down and nothing else does: the queue split, the concurrency caps and where they are enforced, the claim query, and the backpressure signal that stops accepting new work. State the in-flight worker count each class needs at its arrival rate, and what the cap costs the tenant that hits it.

Approach

1. Do the concurrency arithmetic first, because it determines the pool shapes. Required in-flight work is arrival rate times service time: 600/second of 200 ms handlers needs about 120 workers, while one export per second at 180 seconds needs 180 on its own. The long tail dominates any pool it shares, so a single pool sized from the mean is consumed by exports while 50 ms projections queue behind them. That is head-of-line blocking, and adding capacity does not fix it because the ratio is what is wrong.
2. Split by job class rather than by priority. Priority inside one pool still lets a running export hold its worker for the next four minutes - there is no preemption for a handler already executing. Separate queues and separate worker processes give each class a floor the other cannot take.
3. Enforce the per-tenant cap inside the claim, not at enqueue. Claim with SELECT ... WHERE status='queued' AND run_after <= now() AND job_type=$1 ORDER BY run_after, job_run_id FOR UPDATE SKIP LOCKED LIMIT 1, restricted to tenants currently under their cap. Derive the running count from rows in status 'running' with a live lease rather than from a counter incremented at claim and decremented at completion: the derived count self-heals when a worker dies, and the counter leaks a slot every time one does.
4. Make the ordering fair over tenants instead of over jobs. Strict FIFO with a cap still forces the claim to scan past 50,000 rows belonging to a capped tenant before it finds anyone else's work, so cost grows with backlog depth. Pick the tenant first from a small per-tenant queue-depth summary, then claim within that tenant, which makes the scan proportional to the number of active tenants instead.

Follow-up

• An export outruns its lease, so two copies run. What must the handler do to make the second copy harmless, and what does it write to guarantee that?
• With a cap of 4, the tenant's 50,000 exports now take hours. What do you tell them, and does the answer change if they are the largest tenant on the platform?

p99 jumped on one listing filter while p50 stayed flat

Medium · query planning · composite index · percentiles

After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.

Approach

1. Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
2. Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
3. Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
4. Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.

Follow-up

• The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
• How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?

Suggested practice checklist

Built from the rounds and topics Kantar candidates report.

Day 1 — Map the Kantar loop

• Write out the reported sequence: Initial Screening Call, Technical Rounds, Behavioral 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 Object-Oriented Programming (OOP)

• Spend the session on Object-Oriented Programming (OOP), which Kantar candidates report being tested on.
• Write one worked example in Object-Oriented Programming (OOP) and time yourself on it.

Deliverable: One timed worked example in Object-Oriented Programming (OOP).

Day 3 — Work Databases (DBMS)

• Spend the session on Databases (DBMS), which Kantar candidates report being tested on.
• Write one worked example in Databases (DBMS) and time yourself on it.

Deliverable: One timed worked example in Databases (DBMS).

Day 4 — Work SQL (Querying)

• Spend the session on SQL (Querying), which Kantar candidates report being tested on.
• Write one worked example in SQL (Querying) and time yourself on it.

Deliverable: One timed worked example in SQL (Querying).

Day 5 — Answer out loud: Technical Fundamentals

• Answer aloud, timed: Explain the difference between Call by Value and Call by Reference.
• Answer aloud, timed: How do you manage Object-Oriented Programming (OOPs) principles in your code?

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

Day 6 — Answer out loud: Coding and Problem Solving

• Answer aloud, timed: Write a function to perform basic operations on an Array.
• Answer aloud, timed: Solve a medium-level LeetCode style problem involving strings or greedy algorithms.

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

Day 7 — Answer out loud: Behavioral and Role Fit

• Answer aloud, timed: Describe a challenging project from your past and your specific contribution to it.
• Answer aloud, timed: How do you handle situations where there is a disagreement on technical direction within your team?

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

Behavioural preparation

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

Describe a challenging project from your past and your specific contribution to it.

Medium · Behavioral and Role Fit

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 situations where there is a disagreement on technical direction within your team?

Medium · Behavioral and Role Fit

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 us about a time you had to learn a new technology quickly to solve a problem.

Medium · Behavioral and Role Fit

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 prioritize tasks when you are working under tight deadlines?

Medium · Behavioral and Role Fit

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 apply your engineering skills specifically to the data and insights domain?

Medium · Behavioral and Role Fit

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?