Top 10 LeetCode Patterns Every Junior Developer Must Know Before Their FAANG Interview
Most junior developers preparing for FAANG interviews make the same mistake: they grind through hundreds of individual LeetCode problems without ever stepping back to ask what pattern is this actually testing?
The result is a catalog of memorized solutions that falls apart the moment an interviewer introduces a slight twist. The better approach — the one that separates candidates who pass FAANG loops from those who repeat them — is mastering the 10 core patterns that underpin the vast majority of coding questions you will ever face.
These are not shortcuts. They are the mental frameworks that let you recognize a new problem as a variant of something familiar, choose the right data structure in under 30 seconds, and spend your interview time communicating a clear solution instead of searching for one.
Why Patterns Beat Problem-Grinding
LeetCode has over 2,000 problems. No one solves them all. But virtually every problem on that list — and in real FAANG interviews — is a variation on one of roughly 15 underlying patterns. FAANG interviewers are not testing whether you have seen a specific problem before. They are testing whether you can identify the structure of an unfamiliar problem and apply a known strategy to it.
The 10 patterns below cover the vast majority of medium-difficulty problems you will encounter at Google, Meta, Amazon, Apple, and Netflix. Learn to recognize them cold. Then practice applying each one to at least three problems from memory before your interview.
The 10 Patterns
Sliding Window
01A sliding window is a contiguous subarray or substring of variable or fixed size that you move across the input. FAANG uses it to test whether you can avoid brute-force O(n²) nested loops on problems involving contiguous ranges — if you reach for a nested loop on an array, there's usually a sliding window lurking.
Two Pointers
02Two pointers — one starting at each end of a sorted array, or both starting at the same end at different speeds — collapse an enormous variety of search and comparison problems into O(n) solutions. FAANG interviewers love it because it filters candidates who default to nested loops.
Fast & Slow Pointers
03Also called Floyd's cycle-detection algorithm: one pointer moves one step at a time, the other moves two. When a cycle exists they must eventually meet. FAANG uses this pattern for linked list problems and any scenario where you need to find a cycle, the middle node, or a repeated element without extra memory.
Merge Intervals
04Sort intervals by start time, then walk through them checking whether each new interval overlaps the last. It sounds simple but cleanly handles overlapping, touching, and nested ranges — a staple of calendar and scheduling problems that come up repeatedly at Google and Meta.
Cyclic Sort
05When you're given an array containing numbers in the range 1 to n and asked to find missing or duplicate values, cyclic sort lets you do it in O(n) time and O(1) space by placing each number at its correct index. FAANG tests this to see if you know in-place algorithms beyond the standard sort call.
Tree BFS / DFS
06Binary trees appear in roughly 30% of FAANG coding rounds. DFS (pre/in/post-order via recursion or a stack) solves path, depth, and serialization problems. BFS (level-by-level via a queue) solves level-order, minimum depth, and closest-node problems. Know both cold — pick wrong and you'll spend interview time recovering.
Two Heaps
07Maintaining a max-heap of the lower half and a min-heap of the upper half of a running dataset lets you find the median in O(log n) per insert — a pattern Amazon and Microsoft reach for on streaming-data and scheduling problems. The insight is using two heaps to keep the middle of a dataset cheaply accessible.
Subsets / Backtracking
08Backtracking explores all possible states by building a solution incrementally and undoing choices that lead to dead ends. It's the canonical approach for generating combinations, permutations, and subsets. FAANG uses it to test whether you understand recursive state-space search versus brute force.
Binary Search
09Binary search isn't just for sorted arrays — it applies anywhere the search space has a monotonic property (i.e., if an answer x works, every value above or below it also works). FAANG interviewers use modified binary search to probe whether candidates can generalize beyond the textbook version.
Top K Elements
10When a problem asks for the K largest, smallest, or most frequent elements, a heap of size K is almost always the right tool: maintain it in one pass for O(n log K) rather than a full sort at O(n log n). FAANG applies this pattern constantly in data-stream, frequency, and ranking problems.
How to Actually Learn These Patterns
Knowing a pattern exists is not the same as being able to apply it under interview pressure. The gap between recognition and execution is closed through one thing: deliberate practice with real-time feedback.
For each pattern above, work through three to five problems. Write out your solution, then immediately ask yourself: which part of the pattern did I use? What was the trigger that told me this was the right approach? That metacognitive step is what builds pattern recognition — not just completing the problem.
Once you can identify and solve a pattern alone, simulate interview conditions: solve a new problem in a shared editor while narrating your thinking out loud. The discomfort is the point. If you only practice in silence, you will freeze when someone is watching.
Ready to apply these patterns live? Practice with a FAANG engineer.
PairPass pairs you with a senior engineer from Google, Meta, or Amazon for a live mock interview. You'll work through real problems, get pattern-recognition coaching in real time, and walk away knowing exactly where your gaps are before the real thing.