I jumped straight into the data model which was probably the wrong call.
Start by clarifying requirements and scale, then design the core data model for candidates, jobs, and applications, followed by API design and integration points. Focus on scalability, data consistency, and extensibility for future needs like analytics and AI features.
Pro tip: Emphasize the importance of a flexible data model to handle custom workflows and fields, as different companies have unique hiring processes. Also, discuss how to ensure data privacy and compliance (e.g., GDPR) from the start.
Ask about scale (number of users, jobs, applications), key features (job posting, candidate tracking, interview scheduling, reporting), and non-functional requirements (availability, latency, consistency).
Define core entities: Candidate, Job, Application, Interview, User (recruiter/hiring manager), and their relationships. Consider using a relational database for strong consistency, but be open to NoSQL for scalability if needed.
Outline RESTful APIs for CRUD operations on entities, plus endpoints for search, filtering, and reporting. Discuss integration with external systems like job boards, calendar services, and email.
Discuss partitioning strategies (e.g., by company or job), caching, read replicas, and asynchronous processing for tasks like resume parsing and email notifications.
Mention authentication/authorization (OAuth, RBAC), data encryption, audit logs, and compliance with regulations like GDPR and CCPA.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.