Type · algorithmic

How to Pass the Serviceplan Group Software Engineer Interview in 2026
The Serviceplan Group DNA (TL;DR)
The Serviceplan Group Interview Loop
Your onsite loop will typically consist of 5 rounds.
- 1
Round 1
Recruiter ScreenMotivation, role fit, logistics. - 2
Round 2
Coding ScreenLeetCode-medium algorithmic problems under time pressure. - 3
Round 3
System DesignDistributed systems, trade-offs at scale, architecture under constraints. - 4
Round 4
Onsite CodingLeetCode-hard, debugging, code clarity, edge cases. - 5
Round 5
Behavioral / LeadershipPast evidence of ownership, influence, resolving conflict.
The Danger Zone: Top Reasons Candidates Fail
Based on our database of Serviceplan Group interview outcomes, avoid these common traps:
- Describing a situation where the conflict was never truly resolved.
- Not reaching a constructive resolution or damaging the relationship.
- Not considering data quality checks and error handling within the pipeline.
- Not handling edge cases like zero impressions or invalid timestamps.
Get the full Serviceplan Group playbook, free
Every round, the exact grading rubric interviewers score against, all the questions, and unlimited mock-interview practice. Free account, no credit card.
Test Yourself: Real Serviceplan Group Questions
Three real prompts pulled from our database.
Type · Conflict Resolution
Type · architecture
+ many more questions, signals, and worked examples
Sign up to unlock the full Serviceplan Group grading rubric
Serviceplan Group Interview Question Bank
A sample from our database, grouped by round. Sign up to see the full set.
9 of 17 questions shown
Recruiter Screen
1- 1
Type · motivation
What interests you about working at Serviceplan Group, particularly within our advertising technology and data analytics domain?
Coding Screen
3- 2
Type · algorithmic
Given a dataset of user interactions with online ads (impressions, clicks, conversions) and their associated timestamps, design an algorithm to calculate the click-through rate (CTR) for each ad campaign within a given time window. Consider efficiency for large datasets. - 3
Type · algorithmic
Implement a function that takes a list of ad creatives (each with a unique ID, start date, and end date) and a specific date, and returns all creatives that are active on that date. Optimize for scenarios where the list of creatives is very large and queries are frequent. - + 1 more questions in this round (sign up to unlock)
System Design
3- 4
Type · architecture
Design a system to serve personalized ad recommendations to users across different platforms (web, mobile app). Consider aspects like user profiling, real-time bidding, ad inventory management, and scalability to handle millions of users and ad requests per second. - 5
Type · architecture
Design a data pipeline to collect, process, and analyze user engagement data (clicks, views, time spent) from various advertising channels. The pipeline should support near real-time reporting for campaign managers and batch processing for long-term trend analysis. - + 1 more questions in this round (sign up to unlock)
Onsite Coding
4- 6
Type · algorithmic
You are given a stream of ad impression events, each with a user ID and a timestamp. Design a system to detect 'ad fraud' by identifying users who generate an unusually high number of impressions within a very short time window (e.g., more than 100 impressions in 1 minute). Discuss how you would handle the streaming nature of the data and potential edge cases. - 7
Type · code-quality
Refactor the following Python code snippet, which is used to calculate the effective cost per mille (eCPM) for ad placements, to improve its readability, maintainability, and robustness. Pay attention to variable naming, error handling, and modularity. - + 2 more questions in this round (sign up to unlock)
Behavioral / Leadership
6- 8
Type · ownership
Tell me about a time you identified a significant technical debt or performance bottleneck in a system you were working on. What steps did you take to address it, and what was the outcome? - 9
Type · collaboration
Describe a situation where you had a technical disagreement with a colleague or team member. How did you approach the discussion, and what was the resolution? - + 4 more questions in this round (sign up to unlock)
Unlock all 17 Serviceplan Group questions, free
No credit card. Every question with its framework, the grading signals interviewers score against, and a worked answer for each.
Interview tracks at Serviceplan Group
How Serviceplan Group's DNA translates across functions. Pick your role.
Compare Serviceplan Group with similar employers
Same DNA, different bar. Browse the closest companies in our database and see how their loops differ.
Dept
Same tierThe Transformation Brand philosophy at Dept guides interviewers to evaluate a candidate's capacity to innovate and de...
See Dept interview questions
Havas
Same tierHavas assesses strategic thinking and client relationship skills, looking for candidates who can translate brand obje...
See Havas interview questions
Publicis Groupe
Same tierPublicis Groupe seeks candidates demonstrating creativity, strategic thinking, client-centricity, and digital acumen....
See Publicis Groupe interview questions
Practice Serviceplan Group interviews end-to-end
Serviceplan Group Mock Interview
Run a live mock interview with our AI interviewer using Serviceplan Group-style prompts. Get scored on structure, signal, and answer length - exactly how the real loop grades you.
Open
STAR Stories for Serviceplan Group Behavioral Rounds
Build a Story Bank of your past wins, mapped to the leadership signals Serviceplan Group interviewers grade on. Reuse them across every behavioral round.
Open
Serviceplan Group Interview Prep Hub
The frameworks behind every Serviceplan Group round: CIRCLES for product sense, hypothesis-driven debugging for analytical, STAR for behavioral. Learn each one in 10 minutes.
Open
Interview Frameworks
CIRCLES, STAR, AARRR, RICE, MECE. The exact frameworks that make Serviceplan Group interviewers nod instead of frown. Step-by-step playbooks with the moves and the pitfalls.
Open
Sample answers
What a strong answer to these Serviceplan Group interview questions shows.
Given a dataset of user interactions with online ads (impressions, clicks, conversions) and their associated timestamps, design an algorithm to calculate the click-through rate (CTR) for each ad campaign within a given time window. Consider efficiency for large datasets.
A strong answer shows: Ability to translate a business metric (CTR) into an algorithmic problem.; Understanding of data structures and algorithms for efficient data processing.; Consideration of scalability and performance..
Tell me about a time you had a significant disagreement with a colleague or manager regarding a marketing project. How did you approach the situation, and what was the resolution?
A strong answer shows: Focus on understanding different perspectives.; Proactive approach to conflict resolution.; Ability to maintain professional relationships..