I started with the data model because that felt like the safest ground.
Start by clarifying functional and non-functional requirements, then design a data model that captures the relationships between companies, restaurants, managers, employees, and shifts. Propose a scalable architecture that supports weekly schedule publishing and employee viewing, and discuss trade-offs around consistency, availability, and performance.
Pro tip: Emphasize the publish/view workflow: schedules are drafted and edited by managers, then atomically published on Friday, which requires versioning and notification mechanisms. Also mention how you'd handle shift swaps and time-off requests, as these are common real-world extensions.
Ask about scale (number of restaurants, employees per restaurant), access patterns (managers editing vs. employees viewing), and constraints (e.g., labor laws, shift overlaps).
Define core entities: Company, Restaurant, Employee, Manager, Shift, Schedule, and their relationships. Consider using a relational database for strong consistency and complex queries.
Outline key operations: create/edit schedule, publish schedule, view schedule, request shift swap. Specify how publishing triggers notifications and makes the schedule immutable for employees.
Propose a service-oriented architecture with separate services for scheduling, notifications, and user management. Discuss caching, read replicas, and sharding by company or region.
Discuss consistency vs. availability (e.g., during publish), handling concurrent edits, time zone differences, and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Broke it into a few services: an authoring service for managers to draft and edit schedules, a publishing service that handles the Friday release, a read service for employees, and a notification service.
Start by clarifying the core requirements and scale (e.g., number of employees, schedules per day, real-time needs), then propose a high-level service decomposition aligned with the four functional areas. Walk through the data flow and key trade-offs (e.g., consistency vs. availability, sync vs. async) for each service, and justify your choices based on Gusto's domain (payroll, benefits, HR).
Pro tip: Emphasize idempotency and auditability for schedule publishing and notifications, since payroll-related systems demand reliability and traceability. Also, mention how you'd handle time zones and DST for employee schedules—a common pitfall in workforce management.
Ask about scale (number of employees, schedules per day), latency requirements, consistency needs, and integration points (e.g., payroll, HRIS). Confirm whether schedules are created by managers or automated.
Outline separate services for schedule creation (CRUD, validation), publishing (versioning, conflict resolution), employee access (read APIs, mobile-friendly), and notifications (event-driven, multi-channel). Explain how they interact via APIs or events.
Describe the core entities (Schedule, Shift, Employee, Notification) and relationships. Choose databases (e.g., relational for schedules, NoSQL for notifications) and justify based on access patterns and consistency requirements.
Address trade-offs like synchronous vs. asynchronous publishing, push vs. pull for employee access, and at-least-once vs. exactly-once notification delivery. Explain how you'd scale each service (e.g., sharding by company, caching).
Mention idempotency for publishing, retries with backoff for notifications, and audit logs for compliance. Discuss handling time zones, DST, and schedule conflicts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The Friday spike is the interesting constraint.
Start by clarifying the scale and requirements, then propose a read-optimized architecture with caching and read replicas, and a write-optimized pipeline for Friday spikes using asynchronous processing and sharding. Discuss trade-offs and how you would validate the design with metrics and load testing.
Pro tip: Quantify the read-to-write ratio and peak-to-average traffic to show you think in numbers, and mention that you'd monitor cache hit rate and queue depth to dynamically adjust resources.
Ask about expected read QPS, write QPS on Fridays, data size, latency SLAs, and consistency requirements to ground your design.
Propose caching (e.g., Redis) for frequently accessed schedules, read replicas for database scaling, and possibly a CDN for static assets.
Use asynchronous processing with a message queue (e.g., Kafka, SQS) to decouple schedule publishing from downstream writes, and consider sharding by manager or company ID.
Discuss eventual consistency vs. strong consistency, cache invalidation strategies, and how to handle conflicts or failures during peak writes.
Outline a plan for load testing, monitoring key metrics (latency, error rates, queue depth), and auto-scaling to handle spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.