Big Tech Principal Engineer Interview Prep

Apply the two-pointer technique to solve problems on sorted arrays in O(n) time.

Implement the sliding window pattern for substring and subarray problems.

Use prefix sums to answer range sum queries in O(1) time after O(n) preprocessing.

Explain hash table internals, including collision resolution strategies and their trade-offs.

Solve two-sum and its variations using hash map lookups in O(n) time.

Use frequency counting with hash maps and hash sets for anagram, deduplication, and membership problems.

Apply the monotonic stack pattern to solve next-greater-element problems.

Use queues to implement Breadth-First Search (BFS) traversal patterns.

Perform in-place linked list reversal.

Apply linked list reversal as a subroutine to solve reordering problems (e.g., reorder list, reverse nodes in k-group).

Detect and locate cycles in linked lists using Floyd's tortoise and hare algorithm.

Implement an LRU cache using a hash map and a doubly-linked list.

Implement recursive and iterative DFS traversals (preorder, inorder, postorder) for binary trees.

Apply BFS for level-order traversal and level-based computations.

Prove key BST properties and apply them to implement efficient search and validation operations.

Implement insertion and deletion operations in a balanced Binary Search Tree.

Calculate tree diameter and determine if a tree is height-balanced using a bottom-up recursive approach.

Solve lowest common ancestor (LCA) problems for both binary trees and BSTs.

Represent graphs using adjacency lists and matrices, and analyze their space-time trade-offs.

Implement DFS traversal and apply it to find connected components and detect cycles.

Implement BFS for finding the shortest path in unweighted graphs.

Apply Dijkstra's algorithm for shortest paths in weighted graphs with non-negative edges.

Implement topological sort using both Kahn's algorithm (BFS-based) and a DFS-based approach.

Implement the Union-Find data structure with path compression and union by rank optimizations.

Implement the recursive backtracking template to generate permutations, combinations, and subsets.

Apply pruning techniques to optimize backtracking for constraint satisfaction problems (e.g., N-Queens).

Define the principles of greedy algorithms: greedy-choice property and optimal substructure.

Apply an exchange argument to formally prove the correctness of a greedy algorithm.

Distinguish problems solvable by greedy algorithms versus those requiring dynamic programming.

Solve classic interval problems (merging, scheduling) using greedy strategies.

Identify DP problems by recognizing optimal substructure and overlapping subproblems.

Convert a brute-force recursive solution to a memoized (top-down) DP implementation.

Transform a top-down DP solution to a bottom-up (tabulation) implementation with proper state ordering.

Solve foundational 1D DP problems on sequences (e.g., climbing stairs, house robber).

Solve unbounded and 0/1 knapsack-style problems (e.g., coin change, partition equal subset sum).

Solve 2D DP problems on grids and sequences (e.g., unique paths, longest common subsequence).

Implement a Trie (prefix tree) and its core operations for prefix-based string problems.

Apply binary search to find boundaries, insertion points, and optimal values in sorted search spaces.

Explain the properties of a min/max priority queue and its heap implementation.

Implement core binary heap operations (insert, extract-min/max, heapify).

Apply heaps to solve top-K element problems efficiently.

Design a data structure using two heaps to find the median of a data stream.

Structure a 45-minute system design interview with clear phases: requirements, estimation, high-level design, and deep dive.

Systematically gather and clarify functional and non-functional requirements (e.g., scale, latency, availability).

Perform back-of-the-envelope calculations for storage, bandwidth, and QPS to guide design choices.

Communicate a high-level architecture using clear diagrams and concise component descriptions.

Select appropriate deep-dive areas based on interviewer signals and system criticality.

Articulate design trade-offs with respect to the CAP theorem and other distributed systems principles.

Compare and contrast load balancing strategies (L4 vs. L7, various algorithms) in the context of specific service requirements.

Critique and select caching strategies (e.g., write-through vs. write-back) based on specific latency and consistency requirements.

Justify the choice of database sharding and partitioning schemes, and articulate strategies for handling cross-shard transactions.

Debate the merits of different messaging queue semantics (e.g., at-least-once vs. exactly-once) for a given use case.

Design and contrast different rate-limiting architectures (e.g., centralized vs. distributed) and algorithms (e.g., token bucket vs. sliding window log).

Evaluate different data replication and consensus models (e.g., leader-follower vs. Paxos/Raft) for a distributed datastore.

Design a URL shortening service with analytics and high availability.

Design the core components of a real-time chat system with message routing and presence.

Design a social media news feed with ranking, caching, and fan-out strategies.

Design a video streaming platform covering ingestion, transcoding, CDN delivery, and adaptive playback.

Design a ride-sharing dispatch system with real-time matching and ETA computation.

Design a distributed search system with indexing, ranking, and autocomplete features.

Structure responses using the STAR method, focusing on quantifying impact and demonstrating ownership.

Map personal experiences to Amazon's Leadership Principles with multiple concrete examples for each.

Align past project narratives with Atlassian's company values and Google's focus on collaboration and 'Googliness'.

Articulate a compelling technical vision and strategy from a past project, explaining how it aligned with business goals.

Provide examples of influencing without authority across different teams and organizational levels.

Present failure stories that highlight learning, personal growth, and resulting systemic improvements.

Prepare detailed narratives demonstrating mentorship impact and contributions to team growth and development.

Practice discussing complex technical disagreements and how you drove them to a resolution.