Landing a software engineering role at a top tech company requires more than just knowing how to code — you need to solve problems under pressure, communicate your thought process clearly, and demonstrate system design instincts. This 12-week study plan gives you a structured approach to building all three, whether you are targeting FAANG, a mid-size startup, or a remote-first company.
Understanding the Interview Format in 2026
Most technical interviews follow a predictable structure even as specific formats vary between companies. The traditional LeetCode-style algorithm problem remains dominant at large tech companies, though many firms have shifted toward take-home projects combined with lighter live coding rounds. Understanding what each company values is the first step to preparing efficiently.
Expect 2–4 rounds of algorithmic coding problems, 1–2 rounds of system design (for mid-level and senior roles), and 1–2 rounds of behavioral interviews. Each coding round is typically 45 minutes: 5 minutes for introductions, 30 minutes for the problem, and 10 minutes for your questions. Some companies now use async video submissions where you record yourself solving problems without an interviewer watching live.
- Arrays and Hashing — Appears in ~30% of all problems (two-sum, contains duplicate, group anagrams)
- Two Pointers — ~10% (valid palindrome, 3-sum problems)
- Sliding Window — ~8% (maximum sum subarray, long substring without repeat)
- Linked Lists — ~8% (reversals, cycle detection, merge sorted lists)
- Binary Search — ~8% (search in rotated array, median of sorted arrays)
- Trees and Graphs — ~15% combined (BFS/DFS, path finding, graph traversal)
- Dynamic Programming — ~12% (but most feared — deserves focused study)
- Recursion and Backtracking — ~9% (permutations, combinations, sudoku)
Week 1–4: Data Structures and Fundamentals
The foundation of every algorithm problem is knowing your data structures cold. Before attempting any problem-solving, ensure you can implement each structure from scratch, explain its time and space complexity, and describe when you would choose it over alternatives.
Week 1: Arrays and Hashing
Arrays provide O(1) random access but O(n) insertions and deletions. Hash tables (objects in JavaScript, dicts in Python) provide average O(1) lookups, insertions, and deletions at the cost of extra memory and lost ordering. Many array problems become trivial once you recognize they can be solved with a hash table.
Practice problems: Two Sum, Contains Duplicate, Valid Anagram, Group Anagrams, Top K Frequent Elements, Product of Array Except Self, Longest Consecutive Sequence
Week 2: Two Pointers and Sliding Window
Two pointers are powerful for sorted arrays — one pointer at each end moving toward each other can solve palindrome and pair problems in O(n). The sliding window pattern maintains a "window" of elements and slides it across the array, computing results incrementally rather than recomputing from scratch at each position.
Practice problems: Valid Palindrome, Two Sum II, 3Sum, Container With Most Water, Longest Substring Without Repeating Characters, Minimum Size Subarray Sum, Best Time to Buy and Sell Stock
Week 3: Linked Lists
Linked lists sacrifice random access for faster insertions and deletions at known positions. The critical skill here is pointer manipulation — being able to draw out the list and trace pointer changes prevents getting lost in abstract pointer algebra. Always consider the dummy head node technique for problems that involve adding or removing nodes at the head.
Practice problems: Reverse Linked List, Merge Two Sorted Lists, Reorder List, Remove Nth Node From End, Linked List Cycle, Add Two Numbers
Week 4: Stacks, Queues, and Trees
Stacks follow LIFO (last in, first out) and are ideal for matching problems (parentheses, HTML tags). Queues follow FIFO and underpin BFS graph traversal. Trees store hierarchical data; binary trees are most commonly tested. Master recursive traversal (in-order, pre-order, post-order) and iterative traversal using a stack or queue.
Practice problems: Valid Parentheses, Min Stack, Decode String, Binary Tree Level Order Traversal, Invert Binary Tree, Maximum Depth of Binary Tree, Validate Binary Search Tree
Week 5–8: Algorithms and Problem-Solving Patterns
Week 5: Binary Search
Binary search is deceptively simple — find the middle, compare, and halve the search space — but its applications extend far beyond sorted array lookups. It applies to any monotonic search space where you can determine which half contains the answer. Common variants include searching rotated sorted arrays, finding boundaries (first/last position), and search in answer space (e.g., minimum speed to finish on time).
Practice problems: Binary Search, Search in Rotated Sorted Array, Find Minimum in Rotated Sorted Array, Find Peak Element, Search a 2D Matrix, Koko Eating Bananas
Week 6: Graphs and BFS/DFS
Graphs model pairwise relationships and appear in surprising places: social networks, road maps, dependency resolution. BFS explores layer by layer and finds shortest paths in unweighted graphs. DFS explores as deep as possible before backtracking and is ideal for connectivity, cycle detection, and topological sorting. Represent graphs with adjacency lists for efficiency.
Practice problems: Number of Islands, Clone Graph, Walls and Gates, Rotting Oranges, Pacific Atlantic Water Flow, Number of Connected Components, Word Ladder
Week 7: Dynamic Programming
Dynamic programming is the most feared topic because it requires recognizing when a problem has optimal substructure and overlapping subproblems — the two criteria that make DP applicable. Start with the recursive approach (top-down with memoization) before converting to the iterative bottom-up tabulation form. The key skill is defining the DP state clearly before attempting to solve.
Practice problems: Climbing Stairs, Coin Change, Longest Increasing Subsequence, Longest Common Subsequence, Word Break, Combination Sum IV, House Robber, Partition Equal Subset Sum
Week 8: Recursion, Backtracking, and Sorting
Backtracking explores all possibilities by building solutions incrementally and abandoning ("backtracking") partial solutions that cannot lead to a valid solution. It is the natural approach for permutation and combination problems. Merge sort and quick sort are the sorting algorithms most commonly asked about — understand their O(n log n) average case, their space complexity, and their worst-case scenarios.
Practice problems: Subsets, Subsets II, Combinations, Permutations, Permutations II, Combination Sum, Sudoku Solver, Merge Intervals, Insert Interval
Week 9–10: System Design Basics
System design interviews ask you to design a real-world system — a URL shortener, a chat application, a rate limiter, a distributed cache. The goal is not a perfect design but demonstrating that you can think through tradeoffs, scale considerations, and component interactions. Start with understanding how the internet works at a high level: DNS, load balancers, CDNs, databases, caches, message queues, and worker processes.
The standard framework for system design questions: clarify requirements and scope → propose a high-level architecture → dive into specific components → identify potential bottlenecks and mitigation strategies. Even if you are unfamiliar with the specific product, applying this framework systematically demonstrates engineering maturity.
Core Concepts to Master
- Horizontal vs. vertical scaling: Adding machines vs. adding power to existing machines
- Load balancing: Round-robin, least connections, IP hash; sticky sessions
- Caching: Redis/Memcached; cache-aside, write-through, write-behind patterns; TTL and eviction policies
- Database: SQL vs. NoSQL; replication (master-slave, multi-leader); sharding strategies; CAP theorem tradeoffs
- CDNs: Edge caching, static asset delivery, geographic distribution
- Message queues: Decoupling producers and consumers; at-least-once vs. exactly-once delivery; Kafka vs. SQS
Week 11: Behavioral Preparation
Behavioral interviews evaluate how you work with others, handle conflict, grow from mistakes, and align with company values. The STAR method (Situation, Task, Action, Result) is the standard framework for answering experience-based questions. Prepare 5–6 concrete stories that demonstrate leadership, overcoming challenges, handling disagreements, and delivering under pressure.
Research the company's values and weave them into your answers naturally. "Tell me about yourself" is almost always the opening question — prepare a 90-second pitch that covers your background, your most relevant experience, and why you are excited about this specific role. Practice out loud; hearing yourself speak the words reveals filler phrases and disorganized thoughts that silent review misses.
- Tell me about a time you had a conflict with a teammate and how you resolved it
- Describe a time you received critical feedback and how you responded
- Tell me about a project you are most proud of
- Describe a time you had to learn something quickly under pressure
- Why do you want to work at this company / for this team?
- Where do you see yourself in 3–5 years?
Week 12: Mock Interviews and Review
In the final week, simulate real interview conditions. Use Pramp or Interviewing.io for free peer mock interviews. Do at least 3–4 full 45-minute sessions under conditions that mimic the real experience — no keyboard shortcuts, no looking at hints mid-problem, time pressure included. Review every problem you struggled on and identify the specific concept or pattern that would have unlocked it.
Prepare a "cheat sheet" of things you want to remember: edge cases to check (empty input, single element, duplicates), patterns that recur across problems, and phrases that communicate your thinking clearly to interviewers. Review this sheet the night before each real interview.
Recommended Study Resources
| Resource | Type | Best For |
|---|---|---|
| LeetCode | Problem Bank | Algorithm practice with difficulty ratings and company tags |
| NeetCode 150 | Study Plan | Curated 150 essential problems in a logical order |
| NeetCode System Design | Video Course | System design explained clearly for interview prep |
| Excalidraw | Visual Tool | Draw diagrams during system design interviews |
| Pramp | Mock Interviews | Free peer-to-peer technical mock interviews |
| Interviewing.io | Mock Interviews | Anonymous mock interviews with engineers at top companies |
| Grokking the System Design Interview | Course | Comprehensive system design patterns and case studies |
Do
- Communicate your thought process before, during, and after coding
- Ask clarifying questions before diving into a solution
- Start with a brute-force approach, then optimize
- Trace through your code with an example before declaring it done
- Discuss time and space complexity for every solution
- Prepare thoughtful questions for the "any questions for me?" segment
Avoid
- Silent coding — interviewers cannot read your mind
- Jumping straight to code without explaining your approach
- Rejecting feedback or becoming defensive when nudged
- Memorizing solutions — understand the underlying patterns
- Rushing through explanation of your thought process to save time
- Giving up too quickly — a working hint is not a failure
Conclusion
Twelve weeks of focused preparation can take you from intermittent LeetCode sessions to confident technical interview performance. The key is consistency — 2–4 hours of deliberate practice daily beats 10-hour weekend marathons. Understand the patterns deeply rather than memorizing individual problems; the pattern recognition skills that make you effective as a software engineer are exactly the skills being tested. Approach each interview as a learning opportunity, even the ones that do not result in offers — every session refines your communication and problem-solving. Good luck.