Notion Software Engineer Phone Screen Questions
8+ questions from real Notion Software Engineer Phone Screen rounds, reported by candidates who interviewed there.
What does the Notion Phone Screen round test?
The Notion phone screen typically lasts 45-60 minutes and evaluates core Software Engineer fundamentals. Candidates should expect 1-2 algorithmic problems, basic system design discussion at senior levels, and questions about relevant experience. The goal is to confirm technical competence before bringing candidates onsite.
Top Topics in This Round
Notion Software Engineer Phone Screen Questions
## Round 1 - Coding ## Problem Implement a backend for a multi-room chat service. Users can join rooms, send messages, and fetch message history. Each message has a sender, timestamp, and content. ```python from datetime import datetime class ChatService: def create_room(self, room_id: str) -> None: ... def join_room(self, user_id: str, room_id: str) -> bool: ... def send_message(self, user_id: str, room_id: str, content: str) -> dict: # returns message dict with auto-generated timestamp ... def get_history(self, room_id: str, limit: int = 50, before_ts: datetime = None) -> list[dict]: ... def active_users(self, room_id: str) -> list[str]: ... ``` ## Example ``` chat = ChatService() chat.create_room("general") chat.join_room("alice", "general") chat.join_room("bob", "general") chat.send_message("alice", "general", "Hello!") # -> {"id": "uuid", "sender": "alice", "content": "Hello!", "ts": ...} chat.get_history("general", limit=10) # -> [{"sender":"alice","content":"Hello!","ts":...}] chat.active_users("general") -> ["alice", "bob"] ``` ## Follow-ups 1. How would you implement `get_history` with cursor-based pagination for efficient scrolling? 2. How do you handle a user sending a message to a room they haven't joined? 3. How would you push new messages to connected clients in real time (WebSockets vs. SSE vs. long polling)? 4. How do you design the data model if rooms can have thousands of messages and you need fast lookups by time range?
Notion SWE Phone - Document Store
## Problem Design a document store supporting create, read, update, and delete operations with potentially hierarchical document structures. ## Likely LeetCode equivalent No direct equivalent. ## Tags database,design,hash_table,swe
## Round 1 - Coding / SQL ## Problem Build an in-memory database that stores records as key-value rows with typed fields. Support inserting records, querying by field values, updating, and deleting — all through a simple API that mirrors SQL semantics. ```python class InlineDB: def __init__(self): ... def insert(self, table: str, record: dict) -> int: # returns row_id ... def select(self, table: str, where: dict = None, columns: list[str] = None) -> list[dict]: # where: {field: value} — equality filter ... def update(self, table: str, where: dict, new_values: dict) -> int: # returns count of updated rows ... def delete(self, table: str, where: dict) -> int: # returns count of deleted rows ... def count(self, table: str, where: dict = None) -> int: ... ``` ## Example ``` db = InlineDB() db.insert("users", {"name": "Alice", "age": 30, "active": True}) db.insert("users", {"name": "Bob", "age": 25, "active": False}) db.select("users", where={"active": True}, columns=["name","age"]) # -> [{"name":"Alice","age":30}] db.update("users", where={"name":"Bob"}, new_values={"active":True}) db.count("users", where={"active":True}) -> 2 db.delete("users", where={"name":"Alice"}) -> 1 ``` ## Follow-ups 1. How would you add an index on a specific field to make `select` lookups O(1) instead of O(N)? 2. How do you support compound WHERE conditions like `age > 25 AND active = True`? 3. How would you implement transactions so a sequence of inserts/updates is atomic? 4. How do you handle schema enforcement — rejecting records that are missing required fields?
## Problem You are given an arbitrary JSON object (potentially deeply nested) and asked to render it as an interactive, collapsible tree in the browser — similar to Chrome DevTools' JSON viewer. ```js // Signature function renderJSON(container, data) { // container: HTMLElement to mount into // data: any valid JSON value (object, array, primitive) } ``` **Example input:** ```json {"user": {"id": 1, "tags": ["admin", "beta"]}, "active": true} ``` **Expected rendered output (collapsed):** ``` > user: {2 keys} active: true ``` Expanding `user` reveals `id: 1` and `> tags: [2 items]`. ## Follow-ups 1. How do you handle circular references in the JSON object without crashing? 2. What changes are needed to support syntax highlighting (strings vs numbers vs booleans)? 3. How would you make deeply nested paths copyable to the clipboard on click? 4. If the JSON blob is 50 MB, how do you avoid blocking the main thread during render?
## Problem Design the core data structure and merge logic for a collaborative plain-text editor. Two users can edit the same document concurrently; your system must merge their changes without data loss. ```python class Document: def __init__(self, text: str): ... def apply(self, op: 'Operation') -> 'Document': ... def transform(self, op: 'Operation', against: 'Operation') -> 'Operation': ... class Operation: # Sequence of: retain(n), insert(s), delete(n) def __init__(self, ops: list): ... ``` **Example:** ``` Base: "hello" Alice: insert " world" at index 5 -> "hello world" Bob: delete "h" at index 0 -> "ello" Merged result: "ello world" ``` ## Follow-ups 1. What invariant must hold between `base_length` and `target_length` for two operations to be composable? 2. How does your transform handle the case where both users delete the same character? 3. Why might CRDTs be preferred over OT for a distributed system with no central server? 4. How would you checkpoint document history to allow undo across sessions?
## Problem Implement a role-based access control (RBAC) system where roles can inherit permissions from parent roles. Given a user's assigned roles, determine whether they are authorized for a given action on a resource. ```python class RBACSystem: def add_role(self, role: str, parent: str = None) -> None: ... def grant(self, role: str, action: str, resource: str) -> None: ... def assign_role(self, user_id: str, role: str) -> None: ... def can(self, user_id: str, action: str, resource: str) -> bool: ... ``` **Example:** ``` add_role("editor", parent="viewer") grant("viewer", "read", "article") grant("editor", "write", "article") assign_role("alice", "editor") can("alice", "read", "article") -> True (inherited) can("alice", "delete", "article") -> False ``` ## Follow-ups 1. How do you detect and prevent circular role inheritance? 2. If a user has two roles and one explicitly denies an action, should deny override allow? 3. How would you support resource wildcards, e.g. `can(user, "read", "article:*")`? 4. How would you audit all permissions a user has at a given point in time?
## Problem Given a table of sales records, write a function that computes aggregated metrics grouped by one or more columns. The function must support arbitrary grouping keys and multiple aggregation functions (sum, avg, count, min, max) simultaneously. ```python def aggregate(rows: list[dict], group_by: list[str], agg: dict[str, str]) -> list[dict]: # rows: list of flat dicts with string keys # group_by: columns to group on, e.g. ["region", "product"] # agg: {output_col: "sum(revenue)" | "avg(quantity)" | ...} # returns: list of result dicts, one per group pass ``` **Example:** ``` Input rows: [{"region": "US", "product": "A", "revenue": 100}, ...] aggregate(rows, ["region"], {"total": "sum(revenue)", "avg_rev": "avg(revenue)"}) Output: [{"region": "US", "total": 350, "avg_rev": 116.67}, ...] ``` ## Follow-ups 1. How does your implementation scale when rows exceed available memory? 2. How would you add a `HAVING` filter (e.g., only groups where total > 1000)? 3. Can you support window functions like running totals within each group? 4. How would you parallelize the aggregation across CPU cores?
## Problem You are building a project management UI. Implement a `TaskRow` component that displays a single task, supports inline title editing on double-click, and cycles through statuses (Todo -> In Progress -> Done -> Todo) on a button click. ```tsx // React / TypeScript type Status = 'todo' | 'in_progress' | 'done'; interface TaskRowProps { task: { id: string; title: string; status: Status }; onUpdate: (id: string, patch: Partial<{ title: string; status: Status }>) -> void; } export function TaskRow({ task, onUpdate }: TaskRowProps) { ... } ``` **Requirements:** Editing commits on blur or Enter; Escape cancels. Status badge color changes with state. ## Follow-ups 1. How do you prevent accidental edits when a user single-clicks to select text? 2. If 1000 task rows are rendered, what optimizations prevent sluggish re-renders? 3. How would you add an animated transition when the status changes? 4. How would you test that Escape correctly discards unsaved edits?
What to Expect in the Notion Phone Screen Round
The Notion Software Engineer Phone Screen round has a specific calibration purpose distinct from other rounds in the loop. Across 8+ verified reports on LeakCode for this exact round type, the consistent expectations: clear scoping of the problem before diving into a solution, explicit reasoning about complexity, structured handling of edge cases, and the ability to discuss trade-offs between two reasonable approaches.
Reports tagged with the Phone Screen round at Notion show recurring patterns in difficulty and topic distribution. The Phone Screen round is typically 45-60 minutes; the interviewer is calibrated against a specific rubric. The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the answer. It is the path: did you clarify, did you verbalize your approach, did you handle edge cases, and did you communicate throughout.
How To Prepare for This Specific Round
Filter the questions below to the most recent reports (past 6-12 months). Questions tagged for this exact round type from this exact company at this exact role level are the highest-signal data available. Older reports may reference questions that have since rotated out of the company's pool.
Practice 4-6 representative problems from this set under timed conditions. The goal is not memorization (companies rotate questions); the goal is to internalize the patterns the interviewer typically reaches for and the depth of follow-up to expect. Reports on LeakCode also tag the typical follow-up depth at this round type, which is the discriminating signal between hire and no-hire calibration.
Phone Screen Round Timing and Format
The Phone Screen round at Notion typically runs 45-60 minutes. Use the first 2-3 minutes to clarify requirements; you should never start coding or designing without verifying the input/output format, constraints, and edge cases out loud. Use the next 5-7 minutes to verbalize your approach before writing any code. The middle 20-30 minutes are implementation. Reserve the final 10 minutes for testing with concrete examples and discussing optimization or trade-offs.
Time budget discipline is one of the most reliable senior-vs-junior discriminators in this round. Strong candidates verbalize where they are in their budget out loud ("I've used about 20 minutes, I have 15 minutes left for testing and one optimization"). This signals engineering maturity to the interviewer and creates positive feedback they can capture in writing.
Common Failure Modes in This Round
Reports tagged "no hire" at Notion Software Engineer Phone Screen commonly cite: coding silently without verbalizing approach, jumping to implementation before clarifying requirements, missing edge cases (empty input, single element, very large input), producing working code that the candidate cannot refactor when asked, and failing to test their solution with concrete examples before declaring done.
The single most predictive failure mode in 2025-2026 reports: not asking clarifying questions. Interviewers at all FAANG companies are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's notes.
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