How to Crack Navan Coding Interviews in 2026
Complete guide to Navan coding interviews — question patterns, difficulty breakdown, must-practice topics, and preparation strategy.
Navan (formerly TripActions) runs a standard but rigorous technical interview process for software engineering roles. Candidates typically face 3-4 rounds, including a recruiter screen, a technical phone screen, and a virtual onsite consisting of 2-3 coding interviews and a system design/behavioral session. The coding interviews are algorithm-focused, conducted in a collaborative IDE, and require clear communication and optimal solutions.
By the Numbers — Difficulty Breakdown and What It Means
Our data shows a clear pattern in Navan's recent coding interviews: 0% Easy, 67% Medium, and 33% Hard problems across a typical 3-question set. This distribution is telling.
The complete absence of Easy questions means Navan expects a strong baseline proficiency. You won't get a warm-up. The high concentration of Medium problems (two out of three) forms the core of the assessment—these questions test your ability to apply standard data structures and algorithms to moderately complex scenarios under time pressure. The single Hard question (one in three interviews) is the differentiator. It's used to separate competent candidates from exceptional ones, often involving non-obvious optimizations, intricate edge cases, or combining multiple concepts.
In practice, this means your preparation must be efficient. Mastery of Medium problems is the non-negotiable ticket to the next round. A solid performance on the Hard problem is what often secures the offer.
Top Topics to Focus On
The most frequent topics in Navan interviews are Hash Table, Linked List, Design, Doubly-Linked List, and String. Here’s how to prioritize them.
Hash Table: The single most important data structure. It’s the cornerstone for achieving O(1) lookups and is critical for optimizing solutions that would otherwise be O(n²). You must know how to use it for frequency counting, memoization, and storing mappings.
Linked List & Doubly-Linked List: Often tested together. Problems range from basic pointer manipulation (reversal, cycle detection) to more complex list operations requiring a dummy node or two-pointer technique. Doubly-linked list questions frequently involve designing a data structure like an LRU Cache.
Design: This refers to "object-oriented design" or "design a data structure" problems (e.g., design a parking lot, design a logger rate limiter). It tests your ability to translate real-world constraints into clean, maintainable class hierarchies with the right APIs and data structures.
String: A perennial favorite. Navan problems often involve string manipulation, parsing, or matching. Be ready for sliding window, two-pointer approaches, and efficient character counting.
The quintessential Navan pattern combines Hash Tables and Linked Lists to design a high-performance data structure. The classic example is the LRU Cache.
class ListNode:
def __init__(self, key=0, val=0, prev=None, next=None):
self.key = key
self.val = val
self.prev = prev
self.next = next
class LRUCache:
def __init__(self, capacity: int):
self.cap = capacity
self.cache = {} # Hash Table: key -> ListNode
# Dummy nodes for Doubly-Linked List boundaries
self.left = ListNode() # LRU
self.right = ListNode() # MRU
self.left.next, self.right.prev = self.right, self.left
def _remove(self, node):
# Remove node from its current position
prev, nxt = node.prev, node.next
prev.next, nxt.prev = nxt, prev
def _insert(self, node):
# Insert node at MRU position (right before self.right)
prev, nxt = self.right.prev, self.right
prev.next = nxt.prev = node
node.prev, node.next = prev, nxt
def get(self, key: int) -> int:
if key in self.cache:
node = self.cache[key]
self._remove(node)
self._insert(node) # Mark as recently used
return node.val
return -1
def put(self, key: int, value: int) -> None:
if key in self.cache:
self._remove(self.cache[key])
node = ListNode(key, value)
self.cache[key] = node
self._insert(node)
if len(self.cache) > self.cap:
lru = self.left.next
self._remove(lru)
del self.cache[lru.key]
Preparation Strategy — A 4-6 Week Study Plan
Weeks 1-2: Foundation & Core Topics. Spend 60-90 minutes daily. Use a platform like CodeJeet to systematically cover the top topics. Start with Hash Tables and Strings, then move to Linked Lists. For each topic, solve 8-10 Medium problems. Understand the underlying pattern, don't just memorize solutions. Implement the LRU Cache from scratch until you can do it without reference.
Weeks 3-4: Pattern Integration & Mock Interviews. Increase to 2 hours daily. Focus on problems that combine topics (e.g., a String problem solved with a Hash Table and a sliding window). Solve 1-2 Hard problems per week, focusing on the patterns Navan uses (like complex list manipulations or advanced system design for a data structure). Begin weekly mock interviews with a peer or using a timed platform to simulate pressure.
Weeks 5-6: Refinement & Company-Specific Practice. Target 90 minutes daily of focused review. Use the last week for active recall—re-solve your previously marked challenging problems. Dedicate significant time to Navan’s tagged questions on CodeJeet. Practice clearly explaining your thought process out loud as you code, as this is a critical part of Navan's collaborative interview style.
Key Tips
- Communicate Relentlessly. Before writing code, verbally outline your approach, complexity, and edge cases. Ask clarifying questions. Navan interviewers assess your collaboration skills as much as your technical ability.
- Optimize from the Start. With no Easy questions, your first working solution should be near-optimal. Always lead with a Hash Table or other efficient data structure if applicable. Mention a brute force only as a stepping stone to your better solution.
- Master the Doubly-Linked List + Hash Map Combo. This is a high-value pattern for Navan. Be so comfortable with pointer manipulation that you can implement an LRU Cache or similar structure flawlessly under pressure.
- Practice Design Scenarios. Be prepared for an object-oriented design question within a coding round. Think in terms of classes, relationships (has-a, is-a), core methods, and which data structure backs each component for optimal performance.
Success in a Navan interview hinges on deep, practical mastery of Medium-difficulty algorithms and the ability to articulate your problem-solving journey. Focus your preparation on their high-frequency topics, and you'll be well-equipped to handle their challenging question mix.