Technical Fundamentals & Core Skills Topics
Core technical concepts including algorithms, data structures, statistics, cryptography, and hardware-software integration. Covers foundational knowledge required for technical roles and advanced technical depth.
Software Engineering Fundamentals
Covers core principles of how modern software systems are designed, built, and maintained. Topics include the separation between frontend and backend responsibilities, database selection and design, API design and contract thinking, architectural styles such as monolithic and microservice architectures, and cloud platform fundamentals. Candidates should understand nonfunctional requirements including scalability, performance, reliability, security, and maintainability, and be able to explain trade offs when choosing technologies and architectures. Expect discussion of component decomposition, deployment considerations, service communication patterns, and indicators that distinguish junior from senior engineering decisions.
Data Structures and Algorithms Concepts
Assesses familiarity with core computer science fundamentals needed to follow technical conversations and evaluate candidate fit for engineering roles. Topics include common data structures such as arrays, linked lists, stacks, queues, trees, heaps, hash tables, and graphs; algorithmic patterns for traversal, search, and sorting; and asymptotic time and space complexity analysis. Candidates should explain trade offs between approaches and why a particular data structure or algorithm is appropriate for a given problem.
Algorithms and Data Structures
Comprehensive understanding of core data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs, and fundamental algorithms including sorting, searching, traversal, string manipulation, and graph algorithms. Ability to analyze and compare time and space complexity using asymptotic notation such as Big O, Big Theta, and Big Omega, and to reason about trade offs between different approaches. Skills include selecting the most appropriate data structure for a problem, designing efficient algorithms, applying algorithmic paradigms such as divide and conquer, dynamic programming, greedy methods, and graph search, and implementing correct and robust code for common interview problems. At more senior levels, this also covers optimizing for large scale through considerations of memory layout, caching, amortized analysis, parallelism and concurrency where applicable, and profiling and tuning for performance in realistic systems.
Technical Depth and Current Knowledge
Assessment of a candidate's deep technical expertise and up to date hands on knowledge across core engineering domains. Interviewers will probe system design, performance optimization, distributed systems patterns, databases both relational and non relational, caching strategies, messaging and queuing systems, application programming interfaces, cloud infrastructure, observability and monitoring, and relevant programming languages and runtimes. Candidates should be prepared to discuss concrete technical trade offs, debugging and performance tuning approaches, how they research unfamiliar topics to maintain accuracy, and examples of technical decisions they have owned. This topic covers maintaining current technical fluency even in leadership roles and being able to have rigorous technical discussions about architecture and implementation.