Preparing for a Technical Interview: Coding Rounds

Most tech company interview loops include: 1-2 phone screens (45-60 minutes each, one coding problem per screen), 1 system design round (45-60 minutes, for mid-senior roles), 1-2 behavioral rounds (30-45 minutes), and sometimes a take-home project (2-4 hours). FAANG companies (Meta, Amazon, Apple, Netflix, Google) typically have 5-6 rounds in a single on-site day. Startups often have 3-4 rounds spread over 1-2 weeks. Research your specific target companies on Glassdoor and Blind to understand their exact interview structure.

Pick the language you are most fluent in. Python is the most popular choice for coding interviews because of its concise syntax and built-in data structures (dictionaries, sets, list comprehensions). Java is common for enterprise companies and provides strong typing that catches errors. JavaScript works well for front-end and full-stack roles. C++ is preferred for systems-level roles. Most interviewers do not penalize language choice. What matters is demonstrating clear thinking, not syntactic perfection. Use the language where you can code fastest and explain your reasoning most clearly.

Focus on 7 essential data structures: arrays and strings (the foundation, appear in 60% of problems), hash maps and sets (O(1) lookup, used in most optimization problems), linked lists (pointer manipulation), stacks and queues (BFS and DFS foundations), binary trees and BSTs (traversals, recursion), heaps (priority queues, top-K problems), and graphs (BFS, DFS, topological sort). For each data structure, know: when to use it, time and space complexity of operations, and 3-5 classic problems that use it. Cracking the Coding Interview covers all of these with 189 practice problems.

Fourteen patterns cover 90% of coding interview problems: two pointers, sliding window, binary search, BFS, DFS, dynamic programming, backtracking, merge intervals, topological sort, union-find, tries, monotonic stack, greedy algorithms, and bit manipulation. For each pattern, study 3-5 representative problems until you can recognize which pattern applies from the problem statement. NeetCode 150 (free, neetcode.io) organizes problems by pattern and is the most efficient study list. Recognizing patterns is the skill that separates candidates who solve problems in 20 minutes from those who stare at the screen.

Platforms: LeetCode (free tier has 2,000+ problems, premium at 35 USD per month includes company-tagged problems), HackerRank (free, good for beginners), and NeetCode (free, curated list of 150 essential problems with video explanations). Target distribution: 40% easy, 40% medium, 20% hard. Solve 3-5 problems per day. For each problem: spend 20-30 minutes attempting it, then study the optimal solution if stuck. Understand the solution deeply before moving on. Speed comes from pattern recognition, not memorization. Track your progress and revisit problems you struggled with after 1-2 weeks.

Core concepts to study: horizontal vs. vertical scaling, load balancers (distributing traffic across servers), caching strategies (Redis, CDNs), SQL vs. NoSQL databases (when to use each), sharding and partitioning (splitting data across servers), message queues (Kafka, RabbitMQ for async processing), API design (REST, GraphQL), and CAP theorem (consistency, availability, partition tolerance tradeoffs). Resources: System Design Interview by Alex Xu (book, 40 USD, the gold standard), Grokking the System Design Interview (Educative.io, 79 USD), and System Design Primer on GitHub (free).

Common system design questions: design URL shortener, design Twitter/Instagram feed, design a chat system, design a notification service, design YouTube/Netflix streaming, design an e-commerce system, and design a rate limiter. Use this framework for every problem: clarify requirements and constraints (5 minutes), estimate scale (users, requests per second, storage), design high-level architecture (draw components and data flow), deep dive into 2-3 critical components (the interviewer will guide which), discuss tradeoffs and bottlenecks, and propose monitoring and scaling strategies. Practice drawing diagrams on a whiteboard or digital tool.

Behavioral questions assess soft skills using past experiences. Prepare stories for: a project you led and its outcome, a conflict with a colleague and how you resolved it, a time you failed and what you learned, a time you disagreed with your manager, your most impactful technical contribution, a time you worked under a tight deadline, an example of mentoring or helping a teammate, and how you prioritize competing demands. Each story should be 90-120 seconds using the STAR format. Amazon's leadership principles guide is the most comprehensive behavioral framework and applies beyond Amazon.

Every tech company has core values or leadership principles that behavioral interviewers score against. Amazon: 16 leadership principles (customer obsession, bias for action, ownership). Google: Googleyness (intellectual humility, collaboration). Meta: Move fast, be bold. Review your target company's values page and map at least one prepared story to each core value. When answering, reference the value implicitly: I believe in owning outcomes end-to-end, which is why I [specific action]. This alignment signals cultural fit, which is a pass/fail criterion at most tech companies.

Step 1: Clarify (2-3 minutes). Ask about edge cases, input size, expected output format, and constraints. Step 2: Plan (5-7 minutes). Explain your approach verbally before coding. Discuss time and space complexity. Get the interviewer's confirmation before writing code. Step 3: Code (15-20 minutes). Write clean, readable code. Use meaningful variable names. Talk through your logic as you write. Step 4: Test (3-5 minutes). Walk through your code with a simple example, then an edge case. Fix any bugs. This structured approach scores higher than jumping straight into coding, even if your solution is the same.

Interviewers evaluate your thought process as much as your final solution. A candidate who explains their reasoning while arriving at a good solution scores higher than one who silently produces a perfect solution. Narrate: I am considering using a hash map here because we need O(1) lookups. I see the tradeoff with space complexity. Let me think about whether we can optimize that. If you are stuck, say so: I am stuck on the transition between these two states. Let me think about what information I need. Interviewers often provide hints when you articulate where you are stuck. Silence is the enemy of a good interview score. This guide is informational only, not career advice.