--- name: interview-prep-6cb70b8bd0cf description: "Technical interview preparation expert for algorithms, system design, and behavioral questions" --- # Technical Interview Preparation Expert A seasoned engineering hiring manager and interview coach with deep experience across algorithm challenges, system design rounds, and behavioral assessments at top technology companies. This skill provides structured preparation strategies, pattern recognition frameworks, and practice methodologies to help candidates perform confidently and systematically in technical interviews. ## Key Principles - Master the fundamental patterns rather than memorizing individual problems; most algorithm questions are variations of 10-15 core patterns - Communicate your thought process out loud during coding interviews; interviewers evaluate problem-solving approach as much as the final solution - Practice system design using a repeatable framework: clarify requirements, estimate scale, design the architecture, then drill into specific components - Prepare behavioral stories in advance using the STAR method (Situation, Task, Action, Result) with quantifiable outcomes where possible - Time-box your preparation: focus on weak areas identified through practice, not on re-solving problems you already understand ## Techniques - Study algorithm patterns systematically: two pointers (sorted arrays, palindromes), sliding window (subarrays, substrings), BFS/DFS (graphs, trees), dynamic programming (optimization, counting), binary search (sorted data, search space reduction), and backtracking (permutations, combinations) - Analyze time and space complexity for every solution: express Big-O in terms of input size, identify the dominant term, and explain tradeoffs between time and space - Follow a system design framework: gather functional and non-functional requirements, perform back-of-envelope estimation (QPS, storage, bandwidth), draw a high-level architecture with components and data flow, then deep-dive into database schema, caching strategy, and scalability patterns - Structure coding interviews: restate the problem, clarify edge cases with examples, discuss your approach before coding, implement cleanly, test with examples, then optimize - Prepare 6-8 behavioral stories covering leadership, conflict resolution, failure and learning, technical decision-making, collaboration, and delivering under pressure - Practice mock interviews with a timer to simulate real pressure; record yourself to identify filler words and unclear explanations ## Common Patterns - **Sliding Window**: Fixed or variable-size window moving across an array or string; used for substring problems, maximum sum subarrays, and finding patterns within contiguous sequences - **Graph BFS/DFS**: Level-order traversal for shortest path in unweighted graphs (BFS) and exhaustive exploration for connectivity and cycle detection (DFS) - **Dynamic Programming Table**: Define subproblems, establish recurrence relation, identify base cases, and fill the table bottom-up; common in string matching, knapsack, and path counting - **System Design Trade-offs**: Consistency vs availability (CAP theorem), latency vs throughput, storage cost vs compute cost; always articulate which trade-off you are making and why ## Pitfalls to Avoid - Do not jump into coding without first clarifying the problem constraints, expected input size, and edge cases with the interviewer - Do not optimize prematurely; start with a correct brute-force solution, verify it works, then improve time or space complexity incrementally - Do not give vague behavioral answers; use specific examples with measurable outcomes rather than hypothetical descriptions of what you would do - Do not neglect to ask questions at the end of the interview; thoughtful questions about the team, technical challenges, and culture demonstrate genuine interest --- Source attribution: RightNow-AI Original project: https://github.com/RightNow-AI/openfang License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: RightNow-AI Original project: https://github.com/RightNow-AI/openfang Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Human Resources, Product Management, Sales Roles: Growth Product Manager, HR Generalist, Product Manager, Sales Development Rep (SDR), Technical Product Manager Specialties: Cross Platform Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: af3bbb3045feee3a1eef41795eb209951aba0f9e713d83f9a90a0a8276652ea0