I recently received a verbal offer for the HubSpot New Grad Backend Software Engineer position and wanted to share a detailed breakdown of the entire interview process. When I was preparing, I found surprisingly few recent reports covering HubSpot's newer interview format, especially for backend candidates. Hopefully this recap helps anyone preparing for HubSpot, Atlassian, Canva, Stripe, and other engineering-focused companies that value practical software development skills beyond pure algorithm practice.
Overall, HubSpot's interview process felt very different from many traditional Big Tech interviews. While coding ability is certainly important, the company places significant emphasis on engineering judgment, system design fundamentals, communication skills, and the ability to discuss real-world projects. If your preparation strategy consists entirely of grinding LeetCode, some portions of the process may feel surprisingly challenging.
Application Timeline
My interview process moved fairly quickly compared to many large technology companies:
- Applied online
- Received Online Assessment the next day
- Recruiter reached out approximately two weeks later
- Formal interviews scheduled about two weeks after recruiter screening
- Coding and System Design interviews completed on the same day
- Verbal offer received roughly ten days later
From application submission to verbal offer, the entire process took around six weeks. Compared with companies whose hiring processes can stretch over several months, HubSpot's process felt efficient, organized, and candidate-friendly.
Online Assessment (OA)
Based on conversations with other candidates and reports online, HubSpot appears to use two different Online Assessment formats.
Version 1: Billing System Problem
This is the assessment format that appears most frequently in older interview reports.
The problem revolves around a resource billing system where users consume resources across multiple time intervals, and candidates must accurately calculate final charges. Beneath the business context, the core challenge involves handling overlapping intervals and determining peak concurrent resource usage.
Candidates familiar with the following concepts will likely recognize the underlying pattern:
- Sweep Line Algorithms
- Event Processing
- Interval Merging
- Concurrency Counting
- Timeline Simulation
Although the business scenario initially appears complicated, the algorithmic concepts become relatively straightforward once the problem is modeled correctly.
Version 2: CodeSignal In-Memory Database Assessment
This was the version I personally received.
The entire assessment focused on building an in-memory database system and was divided into four progressively more complex levels. Unlike many traditional coding assessments, the emphasis was placed on software engineering fundamentals and extensible architecture rather than advanced algorithms.
Level 1
The first level required implementing basic storage functionality and establishing the foundation of the database system.
The requirements were relatively straightforward, but this stage was more important than it initially appeared because every subsequent level built directly upon the original implementation. Candidates who rushed through the first stage without considering extensibility often encountered difficulties later.
Level 2
The second level introduced additional database operations:
- Insert
- Update
- Delete
- Query
The technical difficulty remained low, but code organization and maintainability became increasingly important.
Level 3
The third level introduced more production-oriented requirements, including:
- TTL (Time-To-Live)
- Expiration Handling
- Time-Based Queries
- Conditional Retrieval
This stage tested whether the original design could accommodate additional functionality without requiring major refactoring.
Level 4
The final level expanded the system further with more business logic and advanced operations.
Candidates who built flexible architectures early could typically complete the final level with a few additional methods and minor modifications. Those who implemented rigid solutions often found themselves rewriting large portions of their code.
What makes the HubSpot OA particularly interesting is that the challenge is not algorithmic complexity. Instead, it evaluates your ability to design maintainable software and think about abstraction, extensibility, data modeling, and clean architecture from the beginning.
Recruiter Screening
The recruiter conversation was primarily informational and covered topics such as:
- Graduation timeline
- Work authorization status
- Preferred office location
- Interest in HubSpot
- Career goals
- Overview of the interview process
There were no technical questions during this stage. The recruiter was mainly assessing overall fit and ensuring that both sides were aligned on logistics and expectations.
Behavioral Interview
One thing that surprised me was how much emphasis HubSpot places on behavioral discussions.
The questions themselves were fairly standard, but interviewers frequently asked multiple layers of follow-up questions. Superficial answers generally did not go very far.
Most Challenging Project
A significant portion of the conversation focused on project experience:
- Most technically challenging project
- Largest engineering obstacle
- Decision-making process
- Trade-offs considered
- Project outcome
- Lessons learned
I strongly recommend preparing at least one project that you can discuss comfortably for ten to fifteen minutes. Interviewers seemed much more interested in depth than breadth.
AI and Machine Learning Experience
This topic generated significantly more discussion than I expected.
The interviewer asked detailed questions regarding:
- AI tools used
- Project integration
- Architecture decisions
- Evaluation methodology
- Business impact
- Technical trade-offs
Candidates should be prepared to discuss AI-related work beyond simply mentioning tools such as ChatGPT or GitHub Copilot.
A strong answer should include:
- Business problem
- Technical solution
- Implementation details
- Evaluation metrics
- Final outcome
Future Team Expectations
One question that initially sounded simple turned out to be more meaningful than expected:
What are you looking for in your future team?
While it initially felt like a culture-fit question, the follow-up discussion suggested they were evaluating long-term alignment, growth expectations, and team compatibility.
System Design Interview
The System Design and Coding interviews were scheduled on the same day. Each session lasted approximately one hour, making the overall experience fairly intense.
One lesson I learned during preparation is that requirement gathering should be efficient. Many candidates spend too much time repeatedly clarifying requirements. While asking questions is important, excessive clarification can become costly during a limited interview window.
My recommendation:
- Confirm primary requirements
- Clarify scale assumptions
- Identify key user workflows
- Move quickly into architecture design
Topics that commonly arise include:
- API Design
- Service Architecture
- Database Selection
- Caching Strategies
- Horizontal Scaling
- Data Consistency
- Failure Handling
- Bottleneck Analysis
- Trade-Off Discussions
The interviewer seemed most interested in architectural reasoning and decision-making rather than arriving at a perfect final design. Whenever presenting a design choice, be prepared to explain why you selected one approach over another.
Coding Interview
The coding round was moderate in difficulty.
The problems felt much closer to practical software engineering tasks than traditional competitive programming questions.
Interviewers evaluated far more than whether the solution eventually passed all test cases. Areas of focus included:
- Communication
- Problem-Solving Approach
- Code Readability
- Edge-Case Handling
- Testing Strategy
- Iterative Improvement
One thing I noticed was that interviewers appreciated candidates who continuously explained their reasoning while coding. Even when I was uncertain about implementation details, discussing my thought process helped keep the conversation collaborative.
HubSpot appears to value how engineers work through problems, not simply whether they can instantly produce the optimal solution.
Overall Impression
If I had to summarize HubSpot's interview process in a few key points:
- Moderate algorithm difficulty
- Strong focus on engineering fundamentals
- Significant System Design emphasis
- Growing interest in AI-related project experience
- Communication skills matter a lot
- Practical software development mindset is highly valued
Candidates who prepare exclusively through LeetCode may find themselves underprepared for behavioral and system design discussions. On the other hand, candidates with internship experience, backend projects, or real-world engineering exposure will likely find many aspects of the process approachable.
Compared to companies that focus heavily on algorithmic trick questions, HubSpot's interview process felt much closer to evaluating actual software engineers.
Interview Preparation Resources
Many candidates preparing for HubSpot, Stripe, Canva, Atlassian, Datadog, Snowflake, and similar companies discover that the biggest challenge is not necessarily solving interview questions themselves. The real challenge is performing consistently under pressure while balancing coding, behavioral discussions, and system design interviews.
For candidates looking for additional preparation support, check out:
Interview Aid VO & Interview Preparation Services
Their team provides support for Online Assessments, technical interviews, mock interviews, System Design preparation, and Virtual Onsite coaching. Many candidates use these resources to better understand company-specific interview expectations and improve confidence before major interview loops.
Top comments (0)