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Nextroll Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Nextroll, you are at the core of a highly scalable, data-driven platform that powers programmatic advertising and marketing technology globally. Nextroll (formerly AdRoll) operates in a high-throughput, low-latency environment, processing billions of ad requests and massive streams of behavioral data daily. Your work directly impacts the platform's core capability to deliver real-time bidding (RTB) decisions, optimize machine learning models, and provide robust analytics to thousands of customers. Engineers at Nextroll do not just build isolated features; they build resilient systems capable of handling hundreds of thousands of requests per second. Whether you are working on the infrastructure behind RollWorks or AdRoll, you will be tasked with solving complex problems around distributed systems, big data pipeline optimization, and high-performance API design.

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

Focus areas

  • Python
  • Algorithmic Problem Solving
  • System Design

The Nextroll interview process

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

Recruiter Call

A brief call to align on your background and the role’s expectations.

What to demonstrate

  • A brief call to align on your background and the role’s expectations
  • Depth in Python

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 Screen

Involves a timed coding challenge or a live pair-programming session with an engineer.

What to demonstrate

  • Involves a timed coding challenge or a live pair-programming session with an engineer
  • Depth in Python

How to prepare

  • Answer aloud and timed: Implement an efficient caching mechanism (such as an LRU cache) tailored for highly frequent read operations.
  • Answer aloud and timed: Given a stream of incoming data events, design a system component that deduplicates events within a rolling time window.

Virtual Onsite Interview

A comprehensive interview consisting of multiple specialized rounds evaluating coding, system design, and behavioral alignment.

What to demonstrate

  • A comprehensive interview consisting of multiple specialized rounds evaluating coding, system design, and behavioral alignment
  • Depth in Python

How to prepare

  • Answer aloud and timed: Design a real-time bidding (RTB) receiver that can handle 100,000 requests per second with sub-100ms latency.
  • Answer aloud and timed: How would you design a distributed analytical dashboard that tracks ad impressions and clicks in real time?

Where candidates lose points

Think Out Loud

Your interviewer is evaluating your problem-solving process, not just your final code. Verbalize your thoughts, explain why you are choosing a specific data structure, and outline your approach before typing.

Optimize for O(1) Lookups

Ad-tech systems rely heavily on speed. When designing classes or registries, think about how you can structure your internal data storage (e.g., using dictionaries or sets in Python) to achieve constant-time lookups.

Ask Clarifying Questions Early

The interview prompts are intentionally open-ended. Ask about scale, expected input sizes, memory limits, and edge cases before you begin designing your solution.

Keep Your Side-Projects Fresh

Interviewers love to hear about what you build outside of work. Be ready to discuss the architecture, challenges, and learnings of any personal projects you have worked on.

Questions & practice

Implement a "Library" or "Book Registry" class in Python that supports adding, searching, and removing books.

Medium · Practical Coding & Data Structures

Implement a "Library" or "Book Registry" class in Python that supports adding, searching, and removing books. You must ensure that specific lookup operations meet strict runtime complexity requirements (e.g., O(1) time complexity).

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?

Parse a large, unstructured text string representing a collection of objects. Write a robust function to searc

Medium · Practical Coding & Data Structures

Parse a large, unstructured text string representing a collection of objects. Write a robust function to search for specific keywords with custom filtering rules and performance constraints.

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?

Implement an efficient caching mechanism (such as an LRU cache) tailored for highly frequent read operations.

Medium · Practical Coding & Data Structures

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 how to implement a streaming algorithm to sample and analyze millions of user events per second witho

Medium · System Design & Scalability

Describe how to implement a streaming algorithm to sample and analyze millions of user events per second without exhausting system memory.

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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

  1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
  2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
  3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
  4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

  • The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
  • Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Given a stream of incoming data events, design a system component that deduplicates events within a rolling ti

Medium · Practical Coding & Data Structures

Given a stream of incoming data events, design a system component that deduplicates events within a rolling time window.

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?

Design a real-time bidding (RTB) receiver that can handle 100,000 requests per second with sub-100ms latency.

Medium · System Design & Scalability

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 would you design a distributed analytical dashboard that tracks ad impressions and clicks in real time?

Medium · System Design & Scalability

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?

Design a high-capacity, fault-tolerant ingestion pipeline that processes data from webhooks and stores it in a

Medium · System Design & Scalability

Design a high-capacity, fault-tolerant ingestion pipeline that processes data from webhooks and stores it in a data lake for offline machine learning.

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?

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 Nextroll candidates report.

Day 1 — Map the Nextroll loop

  • Write out the reported sequence: Recruiter Call, Technical Screen, Virtual Onsite 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 Python

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

Deliverable: One timed worked example in Python.

Day 3 — Work Algorithmic Problem Solving

  • Spend the session on Algorithmic Problem Solving, which Nextroll candidates report being tested on.
  • Write one worked example in Algorithmic Problem Solving and time yourself on it.

Deliverable: One timed worked example in Algorithmic Problem Solving.

Day 4 — Work System Design

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

Deliverable: One timed worked example in System Design.

Day 5 — Answer out loud: Practical Coding & Data Structures

  • Answer aloud, timed: Implement a "Library" or "Book Registry" class in Python that supports adding, searching, and removing books. You must ensure that specific lookup operations meet strict runtime complexity requirements (e.g., O(1) time complexity).
  • Answer aloud, timed: Parse a large, unstructured text string representing a collection of objects. Write a robust function to search for specific keywords with custom filtering rules and performance constraints.

Deliverable: Spoken answers to 2 reported Practical Coding & Data Structures question(s), under time.

Day 6 — Answer out loud: System Design & Scalability

  • Answer aloud, timed: Design a real-time bidding (RTB) receiver that can handle 100,000 requests per second with sub-100ms latency.
  • Answer aloud, timed: How would you design a distributed analytical dashboard that tracks ad impressions and clicks in real time?

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

Day 7 — Answer out loud: Behavioral & Work Style

  • Answer aloud, timed: Describe a complex technical challenge from a past project or side-project. What trade-offs did you make, and what was the outcome?
  • Answer aloud, timed: How do you handle a situation where a team member strongly disagrees with your architectural proposal?

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

Behavioural preparation

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

Describe a complex technical challenge from a past project or side-project. What trade-offs did you make, and

Medium · Behavioral & Work Style

Describe a complex technical challenge from a past project or side-project. What trade-offs did you make, and what was the outcome?

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 a situation where a team member strongly disagrees with your architectural proposal?

Medium · Behavioral & Work Style

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 deliver a feature under a tight deadline with incomplete requirements. How did

Medium · Behavioral & Work Style

Tell me about a time you had to deliver a feature under a tight deadline with incomplete requirements. How did you prioritize your tasks?

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?

Give an example of how you have mentored a junior engineer or helped level up your team's engineering practice

Medium · Behavioral & Work Style

Give an example of how you have mentored a junior engineer or helped level up your team's engineering practices.

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?