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.
Cryptography and Encryption Fundamentals
Comprehensive understanding of modern cryptography and encryption principles used to build secure systems. Candidates should be able to explain the differences between symmetric and asymmetric encryption, appropriate use cases for each, and common algorithms by full name such as Advanced Encryption Standard and Data Encryption Standard for symmetric ciphers and Rivest Shamir Adleman and elliptic curve based algorithms such as Elliptic Curve Digital Signature Algorithm and Elliptic Curve Diffie Hellman for public key operations. Describe hybrid encryption patterns in which asymmetric cryptography is used to protect a symmetric session key, and discuss block cipher modes of operation including cipher block chaining and authenticated encryption modes such as Galois Counter Mode, as well as the role of initialization vectors and nonces. Cover hash functions and integrity checks with properties such as collision resistance and preimage resistance, message authentication codes, authenticated encryption, and digital signatures for authentication and nonrepudiation. Include high level Public Key Infrastructure concepts including certificates and certificate authorities and how certificates are used to establish trust, together with foundational Transport Layer Security and Secure Sockets Layer principles without requiring deep certificate lifecycle management knowledge. Emphasize key management and operational concerns including secure key generation, secure storage, rotation and compromise handling, randomness and entropy sources, recommended key lengths and algorithm lifecycle considerations, and performance and scalability trade offs. Be prepared to discuss common implementation pitfalls and failures such as weak key sizes, poor random number generation, improper key reuse, and lack of authenticated encryption, plus threat models and practical applications including encrypting data at rest and in transit, secure channels, and signing and verification. Avoid deep mathematical proofs unless specifically requested, but be ready to reason about practical trade offs, algorithm selection, and secure implementation patterns.
Technical Depth and Domain Expertise
Covers a candidate's deep, hands-on technical knowledge and practical expertise in their own specialization and their ability to provide credible technical oversight in that area. Interviewers probe the specific patterns, internals, and constraints of the candidate's domain and how the candidate stays current in the field. The concrete sub-areas vary by specialization: for platform, infrastructure, or backend-systems roles this might mean OS internals (Linux and Windows), networking fundamentals (transport and internet protocols, DNS, routing, firewalls), database internals and performance tuning, storage and I/O behavior, virtualization and containerization, or cloud infrastructure and services; for data, ML, or AI roles this might mean model architectures and training dynamics, distributed training and serving internals, feature and data-pipeline design, or statistical methodology; for other technical specializations (sales engineering, technical support, IT business analysis, and similar) this means the specific systems, tools, and technical trade-offs central to that role's own domain. Regardless of domain, candidates should be prepared to explain architecture and design trade-offs, justify technical decisions with metrics and benchmarks, walk through root cause analysis and debugging steps, describe tooling and automation used for deployment and operations, and discuss capacity planning and scaling strategies relevant to their field. For senior candidates, expect both breadth across adjacent areas and depth in one or two specialized areas, with concrete examples of diagnostics, performance tuning, incident response, and technical leadership. Interviewers may also ask why the candidate specialized, how they built that expertise, how it shaped real technical decisions and trade-offs, expected failure modes and performance considerations, and how the candidate mentors others or drives best practices within their specialization.
Problem Solving and Scenario Analysis
Candidates are expected to demonstrate a systematic, structured approach to analyzing and resolving complex scenarios relevant to their field. 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 multiple solution options, and perform trade-off evaluation that balances impact, feasibility, cost, and risk. Core skills assessed include root cause analysis, structured diagnosis of an incident or issue, and reasoning through realistic scenarios drawn from the candidate's own domain (for example, a technical migration, a process breakdown, a customer escalation, a resourcing conflict, or a policy decision). Candidates should define how they would validate a proposed solution (test cases, acceptance criteria, or success metrics), describe how they would monitor or verify the outcome after implementation, and identify opportunities for improvement, risk mitigation, or automation where applicable. Clear communication of the recommended approach, the expected outcomes, and the rationale behind trade-offs made is essential.
Linux System Administration Fundamentals
Core Linux administration knowledge and hands on operational skills required to install, configure, and maintain Linux systems. Covers user and group management, file permissions and ownership, process management and signals, package management across distributions, the boot process and runlevels or targets, basic systemd service control, filesystem navigation and basic disk management, common system configuration files, shell and command line proficiency, and differences between major enterprise and community distributions. Candidates should demonstrate practical troubleshooting of routine issues, patching and updates, and an ability to perform day to day administration tasks reliably.
File System Internals and Recovery
Comprehensive understanding of file system internals and low level storage behavior and how they affect data recovery and forensic analysis. Topics include how data is organized on storage media, common file system structures and metadata models such as inode like structures and allocation tables, allocation strategies including contiguous, linked, indexed and extent based allocation, fragmentation and its effects, and the meaning and implications of sectors, clusters, slack space and unallocated space. Candidates should be able to explain deleted file recovery and reconstruction techniques including why data can persist until overwritten, how journaling and metadata updates influence recoverability, disk imaging and signature based carving, and practical limitations introduced by encryption and space reclamation and internal garbage collection on solid state drives. Also cover distinctions between device level and file level storage, wear leveling and block remapping on flash based media, differences in mobile device storage versus traditional spinning disk storage, and how file system design decisions impact performance, reliability and recoverability. Prepare to describe practical recovery workflows, forensic acquisition considerations, and why recovery tools inspect specific areas of storage rather than deep operating system internals.
Technical Depth and Current Knowledge
Assessment of how deep a candidate's technical expertise actually runs in their own domain, and how current that knowledge is with today's tools, systems, and practices. Interviewers probe for genuine hands-on depth versus surface familiarity: candidates should be able to explain the core mechanisms behind the systems and tools they work with, articulate concrete trade-offs between competing technical approaches, walk through how they debug or troubleshoot problems in their area, describe how they research and validate unfamiliar topics before relying on them, and give real examples of technical decisions they have owned along with the reasoning behind those decisions. This includes maintaining rigorous technical fluency even in roles that have moved away from daily hands-on work (for example engineering leadership, technical sales, or technical program management), where interviewers may probe whether the candidate can still reason precisely about the underlying systems they oversee, sell, or coordinate.
Technical Problem Solving and Learning Agility
Evaluates a candidates ability to diagnose and resolve technical challenges while rapidly learning new technologies and concepts. Topics include systematic troubleshooting approaches, root cause analysis, debugging strategies, how the candidate breaks down ambiguous problems, and examples of self directed learning such as studying new frameworks, libraries, or application programming interfaces through documentation, courses, blogs, or side projects. Also covers intellectual curiosity, baseline technical comfort, the ability to learn from peers and feedback, and collaborating with engineers to understand architectures and tradeoffs. Interviewers may probe how the candidate acquires new skills under time pressure, transfers knowledge across domains, and applies new tools to deliver outcomes.
Technical Foundation and Self Assessment
Covers baseline technical knowledge and the candidate's ability to honestly assess and communicate their technical strengths and weaknesses. Topics include fundamental infrastructure and networking concepts, operating system and protocol basics, core development and platform concepts relevant to the role, and the candidate's candid self evaluation of their depth in specific technologies. Interviewers use this to calibrate how technical the candidate is expected to be, identify areas for growth, and ensure alignment of expectations between product and engineering for collaboration.
Problem Solving and Structured Thinking
Focuses on the general capacity to approach an unfamiliar or ambiguous problem in a disciplined way, independent of the underlying domain. Core skills include clarifying the actual problem and its constraints before acting, decomposing it into smaller subproblems, recognizing patterns from prior experience, choosing among competing approaches, developing and testing a solution incrementally, weighing trade offs such as cost, risk, effort and correctness, reasoning about edge cases and failure modes, and communicating the thought process clearly to others. In technical roles this often shows up as algorithmic reasoning (selecting data structures, estimating time and space complexity) and systematic debugging. In non-technical roles it shows up as issue-tree style decomposition, hypothesis-driven analysis, and structured decision frameworks under ambiguity. The topic is about the reasoning process itself, not any single domain's toolkit.