You shared a detailed approach to data quality monitoring using Great Expectations and CloudWatch. Describe your experience building backend services with Python and FastAPI, specifically how you have designed and optimized APIs to handle business logic efficiently.

I use Great Expectations to define expectations for each dataset and run them as part of a nightly ETL job; the results are pushed to CloudWatch Logs and metrics for alerting. For the API layer, I build FastAPI endpoints with Pydantic models for validation, and I keep the business logic in service classes that are injected via FastAPI’s Depends. I use async database drivers (asyncpg) and connection pooling to keep I/O non‑blocking. Caching is handled with Redis for idempotent operations, and I employ background tasks for heavy processing to keep the request thread free. I also implement pagination, filtering, and rate limiting to protect the service. Monitoring is done with OpenTelemetry, exporting traces to CloudWatch XRay, and metrics to CloudWatch Metrics. Load testing with Locust identifies bottlenecks; I then add indexes, rewrite queries, or shard data as needed. This end‑to‑end pipeline ensures that business logic runs quickly, errors are caught early, and the system scales with traffic.