How would you design a background job/task queue system in Python (e.g., using Celery) for handling long-running operations?

A task queue decouples request handling from long-running work. The web request enqueues a job and returns immediately; a worker process picks it up and executes it. The core components are a broker, typically RabbitMQ or Redis, which holds the queue; a worker pool, which consumes jobs and runs them; and a result backend, which stores return values and status if you need to query them. Celery is the most common Python implementation, but RQ, Dramatiq, and cloud queues like SQS are valid alternatives. The hard parts are not the happy path. You must design for idempotency, because most brokers guarantee at-least-once delivery, so a job can run twice. You must handle retries with exponential backoff and a bounded attempt count, and route permanently failing jobs to a dead-letter queue. You must set visibility timeouts and task time limits so a stuck job does not block a worker forever. For observability, you need a dashboard such as Flower, task-level tracing, and metrics for queue depth and processing time. The trade-offs are operational complexity, eventual consistency between the request and the job result, and the cost of running worker infrastructure.