Technical interview questions
System Design interview questions
Behavioral interview questions
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Most common HubSpot coding Interview questions

HubSpot is a leading customer relationship management (CRM) platform known for its powerful marketing, sales, and customer service tools. Landing a job at HubSpot means joining a company known for its innovative culture, customer-centric approach, and commitment to employee growth. If you’re preparing for a HubSpot interview, you’re in the right place. In this blog, we’ll explore some of the most common HubSpot interview questions—covering technical (coding), System Design, and behavioral aspects.

Let’s get started!

Technical interview questions

HubSpot interviews often include coding questions that reflect real-world challenges their teams solve, often framed in a LeetCode-style format. You can expect problems that test your understanding of data structures, algorithms, and problem-solving skills. These questions not only assess your technical skills but also how you approach and communicate your solutions.

To succeed, practice a variety of coding problems and understand common patterns. Structured problem-solving approaches, like those covered in the Grokking the Coding Interview Patterns course, can help you tackle these problems effectively.

The following are five of the most frequently asked coding questions at HubSpot, giving you an idea of what to expect in their interviews.

1. Merge sorted array

Problem statement: You are given two integer arrays nums1 and nums2 sorted in non-decreasing order, and two integers m and n, representing the number of elements in nums1 and nums2, respectively.

Your task is to merge nums2 into nums1 as one sorted array. The merged array should also be sorted in non-decreasing order.

Note: nums1 has a size of m + n, where the last n elements are set to 0 and should be replaced by elements from nums2.

Example:

Solution:

One of the optimal solutions is to use a two-pointer approach, starting from the end of both arrays. Since nums1 has extra space at the end, we place the largest elements there by comparing the last elements of nums1 and nums2. We iterate backward, placing the greater element at the last available position in nums1. If any elements remain in nums2 after this process, we copy them to the beginning of nums1. This approach ensures an efficient in-place merge without needing extra space.

Solution code:

PYTHON

 
def merge(nums1, m, nums2, n):
    i = m - 1  # Last element in the initial nums1
    j = n - 1  # Last element in nums2
    k = m + n - 1  # Last position in nums1
    
    while i >= 0 and j >= 0:
        if nums1[i] > nums2[j]:
            nums1[k] = nums1[i]
            i -= 1
        else:
            nums1[k] = nums2[j]
            j -= 1
        k -= 1
    
    # If nums2 is not fully merged, copy the remaining elements
    while j >= 0:
        nums1[k] = nums2[j]
        j -= 1
        k -= 1
        
nums1 = [2, 4, 6, 0, 0]
nums2 = [1, 5]
m,n = 3, 2
merge(nums1, m, nums2, n)
print("nums1: ", nums1, ", m: ", m)  
print("nums2: ", nums2, ", n: ", n)  
print("\noutput:", nums1) # Output: [1, 2, 4, 5, 6]

Merge sorted array

Complexity analysis:

The time complexity of this approach is $O(m+n)$ as we traverse both arrays once. The space complexity is $O(1)$ as we modify nums1 in place without using extra space.

2. Merge intervals

Problem statement: Given an array of intervals where each interval is represented as [starti, endi], merge all overlapping intervals and return a new array containing the non-overlapping intervals that fully cover the original set of intervals.

Example:

Solution:

One of the optimal solutions is to first sort the intervals based on their starting points. Then, we iterate through the sorted list, maintaining a merged list. For each interval, if it overlaps with the last interval in the merged list (i.e., its start is within the last interval’s range), we merge them by updating the end time to the maximum of both intervals. Otherwise, we simply add the interval to the merged list. This ensures all overlapping intervals are combined while keeping non-overlapping ones separate.

Solution code:

PYTHON

​x
 
def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        if merged and merged[-1][1] >= interval[0]:
            merged[-1][1] = max(merged[-1][1], interval[1])
        else:
            merged.append(interval)
    return merged
​
print("The merged intervals for [[1,3],[2,6],[8,10],[15,18]] are:", merge([[1,3],[2,6],[8,10],[15,18]]))  # Output: [[1,6],[8,10],[15,18]]

Merge intervals

Complexity analysis:

The time complexity of this approach is $O(n \log n)$ due to the sorting step, while the merging step runs in $O(n)$ , where n is the number of intervals. The space complexity is $O(n)$ .

3. Two sum

Problem statement: Given an array of integers nums and a target integer target, return the indexes of the two numbers that add up to the target.

Note:

Each input has exactly one solution, and you may not use the same element twice.
Return the answer in any order.

Example:

Solution:

One of the optimal solutions is to use a hash map to store the numbers we have seen along with their indices. As we iterate through the array, for each number, we check if the complement (target - current number) is already in the hash map. If it is, we return the indices of the current number and its complement. Otherwise, we store the current number and its index in the hash map. This approach finds the solution in a single pass.

Solution code:

PYTHON

xxxxxxxxxx
 
def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        if merged and merged[-1][1] >= interval[0]:
            merged[-1][1] = max(merged[-1][1], interval[1])
        else:
            merged.append(interval)
    return merged
​
print("The merged intervals for [[1,3],[2,6],[8,10],[15,18]] are:", merge([[1,3],[2,6],[8,10],[15,18]]))  # Output: [[1,6],[8,10],[15,18]]

Two sum

Complexity analysis:

Each element is processed once, so, it takes $O(n)$ time. The hash map can store at most n elements, so the space complexity of this solution is $O(n)$ .

4. Merge two sorted lists

Problem statement: You are given the heads of two sorted linked lists, list1 and list2. Merge the two lists into one sorted linked list and return the head of the merged list.

Example:

Solution:

Use a two-pointer approach to compare nodes from both lists. Create a dummy node to simplify the merging process. Iterate through both lists, adding the smaller node to the merged list. If one list is exhausted, append the remaining nodes from the other list. This approach ensures an efficient, in-place merge without extra space.

Solution code:

PYTHON

xxxxxxxxxx
 
class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next
​
def build_linked_list(values):
    dummy = ListNode(-1)
    current = dummy
    for val in values:
        current.next = ListNode(val)
        current = current.next
    return dummy.next
​
def print_linked_list(head):
    values = []
    while head:
        values.append(head.val)
        head = head.next
    print("Merged linked list:", values)
​
def mergeTwoLists(l1, l2):
    dummy = ListNode(-1)
    current = dummy
​
    while l1 and l2:
        if l1.val <= l2.val:
            current.next = l1
            l1 = l1.next
        else:
            current.next = l2
            l2 = l2.next
        current = current.next
​
    current.next = l1 if l1 else l2
    return dummy.next
​
# Example usage
if __name__ == "__main__":
    l1 = build_linked_list([1, 2, 4])
    l2 = build_linked_list([1, 3, 4])
    merged = mergeTwoLists(l1, l2)
    print_linked_list(merged)

Merge two sorted lists

Complexity analysis:

Each node is processed once, so the time complexity is $O(n + m)$ , where n is the number of nodes in list1 and m is the number of nodes in list2. As the merge is done in-place without using extra space, the space complexity is $O(1)$ .

5. Maximum number of occurrences of a substring

Problem statement: Given a string s, return the maximum number of times a substring can appear under the following conditions:

The substring length must be between minLength and maxLength.
The substring must contain at most maxUnique different characters.

Example:

Solution:

We slide a window of size minLength across the string and track the frequency of substrings using a hash map. We only check substrings of the smallest valid length (minLength) for efficiency. For each substring, we count unique characters and update the frequency map if it meets the maxUnique condition. Finally, return the highest frequency of any valid substring. This approach limits unnecessary checks for longer substrings.

Solution code:

PYTHON

xxxxxxxxxx
 
from collections import defaultdict
​
def maxFreq(s, maxUnique, minLength):
    freq_map = defaultdict(int)
    max_freq = 0
​
    for i in range(len(s) - minLength + 1):
        substring = s[i:i + minLength]
        unique_chars = len(set(substring))
        
        if unique_chars <= maxUnique:
            freq_map[substring] += 1
            max_freq = max(max_freq, freq_map[substring])
    
    return max_freq
​
# Example usage
s = "aababcaab"
maxUnique = 2
minLength = 3
print("Maximum frequency of valid substring:", maxFreq(s, maxUnique, minLength))

Maximum number of occurrences of a substring

Complexity analysis:

Each substring of minLength is checked once, so the time complexity is $O(n \times minLength)$ , where n is the length of the input string s. The space complexity is $O(n)$ due to the hash map storing frequencies of substrings.

Problem	Description
Meeting rooms II	Given meeting time intervals, find the minimum number of meeting rooms required.
Merge k sorted lists	Merge k sorted linked lists into one sorted linked list.
Top k frequent elements	Given an array, return the k most frequent elements.
Find the k-sum of an array	Find the k-th largest subsequence sum of an integer array.

System Design interview questions

In the System Design interview, HubSpot checks how well you can build fast, reliable systems that can handle growth. So, while preparing, focus on database design, how APIs work, caching, load balancing, and the pros and cons of different solutions. Be ready to explain how your system can handle more users and work smoothly even if something goes wrong.

Let’s look at some example system design questions:

1. Design a URL shortening service (e.g., TinyURL)

Problem: Build a system that converts long URLs into shorter, shareable links.

Key features:

Generate unique short URLs.
Handle redirection efficiently.
Support billions of URLs with low latency.

Design highlights:

Use Base62 encoding to generate unique short URLs.
Implement a distributed key-value store (e.g., Redis) for fast lookups.
Apply consistent hashing to distribute URLs across multiple servers.

2. Design a video streaming service (e.g., Netflix)

Problem: Create a system to stream videos to millions of users worldwide.

Key features:

Adaptive bitrate streaming for different network conditions.
Content delivery across regions.
User authentication and personalized recommendations.

Design highlights:

Use content delivery networks (CDNs) to cache and deliver videos.
Implement microservices for video encoding, metadata management, and user sessions.
Store video chunks in distributed object storage, e.g., AWS S3.

Problem: Build a platform where users can post updates and interact with others.

Key features:

User timelines with real-time updates.
Follow/unfollow relationships.
Like, comment, and share functionality.

Design highlights:

Use fan-out on write to deliver posts to followers’ timelines.
Implement a graph database for managing relationships.
Apply caching (e.g., Redis) for frequently accessed timelines.

4. Design an e-commerce store

Problem: Build a platform where users can post updates and interact with others.

Key features:

Product catalog with search and filters.
Shopping cart and checkout system.
Payment processing and order tracking.

Design highlights:

Use a relational database (e.g., PostgreSQL) for product and order data.
Implement eventual consistency for inventory management using message queues, e.g., Kafka.
Secure payment with third-party payment gateways, e.g., Stripe.

5. Design a global messaging system (e.g., WhatsApp)

Problem: Build a system to handle real-time messaging across millions of users.

Key features:

One-to-one and group messaging.
Read receipts and delivery acknowledgments.
Message storage and retrieval.

Design highlights:

Use WebSockets for real-time communication.
Implement event-driven architecture using Kafka.
Use sharded databases for scalability.

Behavioral interview questions

HubSpot emphasizes its culture code, which values transparency, humility, and adaptability. Be ready to share experiences that reflect these principles.

The behavioral interview typically involves questions framed around the STAR (situation, task, action, result) method, allowing interviewers to gauge how candidates handle challenges, collaborate with others, and maintain composure under pressure.

Let’s look at some commonly asked example behavioral questions that will help you prepare better for your HubSpot interview:

1. Tell us about a time you solved a challenging problem.

Example answer:

Situation: At my previous role, a major client’s campaign was underperforming due to incorrect data tracking.
Task: I was tasked with identifying and fixing the issue quickly.
Action: I audited the tracking system, found a misconfigured analytics tag, and corrected it while collaborating with the data team.
Result: As a result, the campaign’s reporting accuracy improved, and the client saw a 20% increase in conversions within a month.

2. How do you prioritize tasks when working on multiple projects?

Example answer: I use a priority matrix to categorize tasks by urgency and importance. For instance, when managing multiple marketing campaigns, I focus on deadline-driven deliverables first while scheduling regular check-ins for long-term projects. This approach ensures I meet critical deadlines while maintaining progress on ongoing initiatives.

3. Describe a time you worked on a team project.

Example answer:

Situation: In a cross-functional team, I collaborated on launching a new product feature.
Task: My role was to coordinate between engineering and marketing to ensure messaging aligned with technical capabilities.
Action: I facilitated weekly syncs to track progress and resolve roadblocks.
Result: The feature launched on time, increased user engagement by 25%, and strengthened collaboration across departments.

4. What is your approach to giving and receiving feedback?

Example answer: I believe feedback is key to growth. When giving feedback, I focus on being specific and solution-oriented. When receiving feedback, I actively listen, reflect, and apply it. For example, after receiving feedback on improving communication clarity, I adopted concise reporting methods, leading to fewer misunderstandings in team updates.

5. Why do you want to work at HubSpot?

Example answer: I’m inspired by HubSpot’s culture code, especially the emphasis on transparency and autonomy. I admire how HubSpot empowers employees to innovate while prioritizing customer success. I’m excited to contribute to a collaborative environment where I can grow and help deliver solutions that make a meaningful impact.

Refine your skills with AI-powered mock interviews

To boost your chances of success, practice with AI-driven mock interview platforms. These platforms provide realistic coding, system design, and behavioral interview simulations, offering instant feedback to improve your performance.

Recommended resources for practice

Grokking the Coding Interview Patterns: Master 26 essential coding patterns to solve thousands of LeetCode-style questions. Efficiently prepare for coding interviews with the ultimate course created by FAANG engineers.
Grokking the Modern System Design Interview: The ultimate guide to the System Design Interview – developed by FAANG engineers. Master distributed system fundamentals, and practice with real-world interview questions & mock interviews.
Grokking the Behavioral Interview: Whether you’re a software engineer, product manager, or engineering manager, this course will give you the tools to thoroughly prepare for behavioral and cultural questions. But beyond even technical roles, this would be useful for anyone, in any profession.
Grokking the Product Architecture Design Interview: The essential guide to API Design & Product Design Interviews – developed by FAANG engineers. Master product design fundamentals & get hands-on with real-world APIs.

Preparing for a HubSpot interview requires a blend of technical knowledge, System Design insights, and a clear demonstration of interpersonal skills. By practicing these questions and refining your problem-solving strategies, you’ll be well-equipped to succeed.

Good luck with your HubSpot interview!

Company Interview Questions

Most common HubSpot coding Interview questions

Technical interview questions

1. Merge sorted array

2. Merge intervals

3. Two sum

4. Merge two sorted lists

5. Maximum number of occurrences of a substring

Other coding problems

System Design interview questions

1. Design a URL shortening service (e.g., TinyURL)

2. Design a video streaming service (e.g., Netflix)

4. Design an e-commerce store

5. Design a global messaging system (e.g., WhatsApp)

Behavioral interview questions

1. Tell us about a time you solved a challenging problem.

2. How do you prioritize tasks when working on multiple projects?

3. Describe a time you worked on a team project.

4. What is your approach to giving and receiving feedback?

5. Why do you want to work at HubSpot?

Refine your skills with AI-powered mock interviews

Recommended resources for practice

Need help preparing for the interview?

Most common HubSpot coding Interview questions

Technical interview questions

1. Merge sorted array

2. Merge intervals

3. Two sum

4. Merge two sorted lists

5. Maximum number of occurrences of a substring

Other coding problems

System Design interview questions

1. Design a URL shortening service (e.g., TinyURL)

2. Design a video streaming service (e.g., Netflix)

3. Design a social media platform (e.g., Twitter)

4. Design an e-commerce store

5. Design a global messaging system (e.g., WhatsApp)

Behavioral interview questions

1. Tell us about a time you solved a challenging problem.

2. How do you prioritize tasks when working on multiple projects?

3. Describe a time you worked on a team project.

4. What is your approach to giving and receiving feedback?

5. Why do you want to work at HubSpot?

Refine your skills with AI-powered mock interviews

Recommended resources for practice

Need help preparing for the interview?