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.
Problem Solving and Scenario Analysis
Candidates are expected to demonstrate a systematic, structured approach to analyzing and resolving technical and operational scenarios. This includes clarifying the problem statement, eliciting requirements, constraints, and assumptions, and identifying missing information or ambiguous areas. Candidates should decompose complex problems into logical components, prioritize tasks or evidence, generate solution options, and perform trade off evaluation that balances impact, feasibility, and risk. Core skills assessed include root cause analysis, incident diagnosis and forensic investigation, and evaluation of technical customer scenarios such as large scale migrations. Candidates should reason about data consistency and concurrency, security and authentication concerns, and payment and transaction flows when relevant. They should design test cases and acceptance criteria, propose instrumentation and monitoring for verification and observability, and identify opportunities for automation and operationalization. Clear communication of the recommended approach, expected outcomes, and the rationale for choices, including when to use a programming solution versus a query based approach, is essential.
Problem Solving and Analytical Thinking
Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.