Slack coding interview questions

Slack is a workplace communication platform that has redefined team collaboration through real-time messaging, video calls, and seamless integration with hundreds of third-party apps. In July 2021, Salesforce acquired Slack for $27.7 billion—one of the largest software deals in history. The acquisition aimed to create a unified platform for business communication and customer relationship management. Now operating under Salesforce, Slack continues to drive innovation in productivity and teamwork, making it one of the most attractive destinations for developers and engineers looking to make an impact at scale.

Getting hired at Slack is more than just passing technical rounds—it’s about demonstrating a commitment to quality engineering, strong collaboration skills, and a passion for delivering user-first solutions. Slack’s technical interview process generally focuses on three primary areas:

Technical coding interviews: These test algorithmic thinking and code quality.
System Design evaluations: These assess your ability to design scalable, robust software systems.
Behavioral assessments: These explore how you collaborate in teams and align with Slack’s culture.

This blog will explore these categories in detail, offer sample questions, and recommend proven resources like Educative to support your interview prep.

Technical coding interview questions

Slack’s coding interviews are designed to evaluate your core programming knowledge, algorithmic thinking, and your ability to write clean, efficient, and maintainable code under time constraints. The questions focus on standard data structures, algorithms, and logic problems.

Below are some of the coding interview questions to give you an idea of the type of problems typically asked at Slack:

1. Missing number

Problem statement: Given an array nums containing n distinct numbers in the range [0, n], return the only number in the range that is missing from the array.

Solution: One quick solution is to calculate the expected sum of numbers from 0 to ‘n’, which can be computed using the formula n * (n + 1) / 2, and subtract the sum of the array elements—the difference will be the missing number.

While the sum formula is a straightforward solution, a more algorithmically interesting approach involves using the Cyclic Sort pattern. In this method, you iterate through the array and place each element at its correct index—i.e., a number x should ideally be at index x. You continue swapping until all elements are positioned correctly. After sorting, the first index where the number doesn’t match the index reveals the missing number. This approach offers linear time complexity and constant space usage, making it highly efficient.

Following is the Python solution to the missing number problem:

def find_missing_number(nums):
    i = 0
    n = len(nums)
    # First pass: place each number at its correct index using cyclic sort
    while i < n:
        correct = nums[i]
        # If the number is within bounds and not at its correct index, swap it
        if correct < n and nums[i] != nums[correct]:
            nums[i], nums[correct] = nums[correct], nums[i]
        else:
            # Move to next index if already correctly placed
            i += 1

    # Second pass: find the first index where the value doesn't match the index
    for i in range(n):
        if nums[i] != i:
            return i

    # If all numbers are in place, the missing number is 'n'

def main():
    inputnumbers = [[3, 0, 1], 
                    [1, 2]]
    i = 1
    for x in inputnumbers:
        print(i, ".	nums: ", x, sep = "")
        print("\tMissing number: ", find_missing_number(x), sep = "")
        print("\n")
        i+=1

main()

Missing Number

Time and space complexity: The algorithm runs in $ O(n) $ time because each element is placed at its correct index with at most one swap and then scanned once. It uses $ O(1) $ space as all operations are performed in-place without any extra data structures.

2. Clone a directed graph

Problem statement: Given a reference of a node in a directed graph, return a deep copy (clone) of the graph. Each node in the graph contains a value and a list of its neighbors. The graph may contain cycles, self-loops, or even be disconnected.

Solution: To approach this problem, we need to traverse the graph and clone each node and its neighbors. As the graph can contain cycles, a naïve traversal could result in infinite loops or redundant cloning. Therefore, we use a hash map (dictionary) to keep track of visited nodes and their corresponding cloned instances.

There are two popular strategies for solving this problem: depth-first search (DFS) and breadth-first search (BFS). Both involve the same core idea: ensuring each node is only cloned once, using the map to link original nodes to their clones.

Here’s how the DFS-based approach works:

Use a dictionary to map original nodes to their clones.
Traverse the graph starting from the given node.
For each unvisited node, create a clone and recursively clone its neighbors.
Return the clone of the starting node.

Following is the Python solution for the DFS-based approach:

from collections import deque

class Node:
    def __init__(self, val, neighbors=None):
        self.val = val
        self.neighbors = neighbors if neighbors is not None else []

def clone_graph(node):
    # Dictionary to keep track of visited nodes and their clones
    old_to_new = {}

    def dfs(n):
        # If node has already been cloned, return its clone
        if n in old_to_new:
            return old_to_new[n]

        # Create a new clone node and add it to the map
        copy = Node(n.val)
        old_to_new[n] = copy

        # Recursively clone and append all neighbors
        for neighbor in n.neighbors:
            copy.neighbors.append(dfs(neighbor))

        # Return the cloned node
        return copy

    # Edge case: if input node is None, return None
    return dfs(node) if node else None

def print_graph(node):
    visited = set()
    queue = deque([node])
    print(“\tGraph structure:”)
    while queue:
        current = queue.popleft()
        if current in visited:
            continue
        visited.add(current)
        neighbor_vals = [neighbor.val for neighbor in current.neighbors]
        print(f”\t\tNode {current.val}: Neighbors -> {neighbor_vals}”)
        for neighbor in current.neighbors:
            if neighbor not in visited:
                queue.append(neighbor)
    print()

def main():
    # Sample graph:
    # 1 → 2 → 3
    # ↓       ↓
    # →→→→→→→ 4
    node1 = Node(1)
    node2 = Node(2)
    node3 = Node(3)
    node4 = Node(4)

    node1.neighbors = [node2, node4]
    node2.neighbors = [node3]
    node3.neighbors = [node4]
    node4.neighbors = []

    print(“Original Graph”)
    print_graph(node1)

    cloned = clone_graph(node1)

    print(“Cloned Graph”)
    print_graph(cloned)

main()

Clone a Directed Graph-DFS approach

Think smarter, not harder—refer Grokking the Coding Interview Patterns in order to decode patterns that turn complex problems into familiar challenges.

Time and space complexity: The algorithm runs in $ O(n) $ time and uses $ O(n) $ space, where n is the number of nodes in the graph. This means that it visits each node exactly once and stores a clone—mapping for each node in the old_to_new dictionary.

Coding Problem	Problem Statement	Key Concepts Tested
Print all balanced brace combinations	Generate all combinations of balanced {} braces for a given number N.	Recursion, backtracking
Find all sub sets of a given set	Return all possible subsets (the power set) of a given list of integers.	Recursion, power set generation
Find ‘K’ closest points to the origin	Given a list of 2D points, find the k points closest to the origin (0,0).	Sorting, heap (priority queue), distance calculation
Search in a rotated sorted array	Search for a target value in a rotated, sorted array in logarithmic time.	Modified binary search, array manipulation
Implement a queue using two stacks	Design a queue using two stacks that supports enqueue and dequeue operations.	Stack and queue fundamentals, data structure simulation

System Design interview questions

To support interview preparation for backend or platform roles, the table below summarizes five commonly asked System Design questions at Slack, along with their core components, key design considerations, and architectural trade-offs:

Question	Problem Statement	Key Components	Design Considerations	Trade-Offs
Messaging API	Design a scalable and maintainable API to support sending and receiving messages in real time.	– API gateway – Message service – WebSocket server – Message store – Notification trigger	– Horizontal scalability – Schema versioning – Real-time delivery – Message deduplication	– Strong vs. eventual consistency – Real-time vs. batch delivery
File upload & sharing	Build a secure, scalable system for uploading and sharing files in Slack conversations.	– File upload API – Virus scanner – Object storage (S3) – CDN – Access control	– Chunked uploads- Secure download links – Storage quotas – Metadata indexing	– Real-time vs. background scanning – Regional replication cost vs. performance
Notification system	Design a system to deliver cross-platform notifications based on user activity and preferences.	– Event bus (Kafka) – Notification router – Delivery workers – Preference store	– Throttling and retries – User preferences and DND – Alert deduplication – Time zone awareness	– Immediate delivery vs. batching – Spam control vs. responsiveness
Search functionality	Create a search system for messages, files, and users with fast and relevant results.	– Indexer service – Inverted index (Elasticsearch) – Query engine – Ranker	– Access-based filtering – Tokenization – Real-time indexing – Caching frequent queries	– Real-time vs. delayed indexing – Relevance vs. performance tuning
Audit logging system	Build a tamper-proof system to log user and system actions for debugging and compliance.	– Log collector – Streaming pipeline – Immutable storage (WORM) – Indexing layer	– GDPR compliance- Secure write -only storage – Retention and role-based access	– Granularity vs. storage cost – Streaming vs. batch pipeline

From load balancers to distributed queues, the course, Grokking the Modern System Design Interview, breaks down what top tech companies are really looking for.

Behavioral interview questions

The following table highlights commonly asked behavioral interview questions at Slack, along with what they reveal about a candidate. It also lists responses using the STAR method to help you structure your own compelling stories.

Question	What It Reveals	Sample Answer (STAR Method)
Tell me about a time you had to work with a difficult or disruptive person.	Conflict resolution and professionalism	S: A teammate would often dismiss others’ ideas in meetings. T: I wanted to ensure everyone felt heard and valued. A: I spoke with him privately and facilitated better turn-taking in our next sessions. R: The meetings became more collaborative and efficient.
Describe a situation where you had to adapt to a significant change at work.	Flexibility and adaptability	S: Our team switched from Scrum to Kanban mid-project. T: I had to quickly adjust my workflow. A: I restructured my task tracking and coordinated daily syncs. R: We successfully completed the sprint on time with improved visibility.
Can you provide an example of how you handled a challenging project deadline?	Time management and performance under pressure	S: A last-minute spec change came two days before our demo. T: I needed to deliver a stable prototype. A: I reprioritized features, flagged dependencies, and looped in QA early. R: The demo went smoothly, and stakeholders appreciated the key features delivered.
Explain a time when you took the initiative to improve a process or project.	Proactivity and continuous improvement	S: Onboarding new engineers took weeks due to scattered documentation. T: I wanted to streamline this. A: I consolidated guides into a shared wiki and added a checklist. R: New hires onboarded 40% faster and reported higher clarity.
Tell me about a time you failed or made a mistake at work. What did you learn?	Accountability and resilience	S: I pushed a hotfix without adequate testing, which caused a regression. T: I had to fix it quickly and prevent future issues. A: I reverted the change and added pre-deploy checks. R: We implemented better hotfix protocols and improved test coverage.
Describe a situation where you had to deliver tough feedback to a colleague.	Emotional intelligence and communication	S: A junior engineer was consistently missing review standards. T: I had to give constructive feedback without discouraging them. A: I highlighted strengths first, then used specific examples to guide improvement. R: Their pull requests improved significantly over the next two sprints.
Give an example of a goal you reached and how you achieved it.	Planning and execution	S: I set a goal to reduce API latency by 30% over two quarters. T: I identified bottlenecks in our caching and query patterns. A: I introduced Redis caching and async database queries. R: We cut latency by 37% and reduced server load.
Tell me about a time you disagreed with a coworker or manager.	Constructive disagreement and diplomacy	S: My manager wanted to sunset a feature I believed still provided user value. T: I needed to present my case respectfully. A: I collected usage data and customer feedback and proposed a phased transition. R: We retained the feature with minor changes and improved satisfaction.
Describe a time you went above and beyond in your role.	Ownership and initiative	S: A teammate left mid-project, leaving a critical analytics feature unbuilt. T: I volunteered to take it on, despite my other tasks. A: I worked late hours that week and consulted with the data team. R: We launched on schedule and earned praise from product leadership.

When it comes to behavioral prep, Grokking the Behavioral Interview is your go-to-guide for crafting stories that land offers.

Practice like you are already in the interview room

One of the best ways to build confidence and refine your responses is through realistic mock interviews. Whether you’re brushing up on data structures, designing scalable systems, or articulating your leadership experience—mock practice bridges the gap between theory and performance.

For structured, domain-specific practice, check out Educative’s Mock Interview Platform, which offers targeted mock interviews in areas like:

System Design
Coding Interview
MAANG+ Prep
Behavioral Interview
Low-Level Design
API Design
Artificial Intelligence
Generative AI System Design
Advanced System Design

Each mock interview is designed to mirror the challenges you’ll face in competitive interviews at top-tier companies—making it an essential part of your prep.

It is recommended to start with Educative’s freemium mock interviewers, especially the Coding Patterns and System Designs options, for hands-on practice with realistic interview scenarios at no cost.

Preparation resources

The next step is to elevate your preparation with expert-curated resources that dive deep into the most critical interview topics. The following resources are designed to accelerate your preparation:

Grokking Dynamic Programming Interview: Master DP with intuitive patterns and problem breakdowns.
Coderust: Hacking the Coding Interview: Solve classic coding problems with efficiency and clarity.
Data Structures for Coding Interviews: Build confidence in arrays, trees, graphs, and more with interactive walkthroughs.
System Design Deep Dive: Real-World Distributed Systems: Explore how actual systems are architected at scale, from messaging to storage.
System Design Interview Prep Crash Course: Prime yourself to be interview-ready for System Design in record time.

Each of these resources is built to help you think like an engineer, not just a test-taker, so you can go into interviews with real confidence.

Frequently Asked Questions

Slack’s interview process typically includes technical coding rounds, a system design interview, and behavioral assessments to evaluate skills and team fit.

Slack allows candidates to use languages they’re most comfortable with, though Python, Java, and JavaScript are commonly used and well-supported.

The coding problems range from medium to hard, focusing on data structures, algorithms, and problem-solving efficiency under time constraints.

Candidates can expect problems involving arrays, graphs, recursion, and classic patterns like cyclic sort, backtracking, and queue/stack operations.

Interviewers assess clarity, correctness, efficiency, use of appropriate data structures, and whether the code is clean, modular, and easy to maintain.