Could you please explain your approach to ETL design to help me understand your experience better?

My ETL design follows a modular, data‑centric approach that emphasizes scalability, maintainability, and observability. First, I define clear data contracts for each source, specifying schema, quality rules, and change‑data‑capture (CDC) mechanisms. I then build a staging layer that ingests raw data using incremental pulls or streaming, ensuring idempotent writes to avoid duplicates. Transformation logic is encapsulated in reusable functions or dbt models, allowing version control and automated testing. I use a workflow orchestrator like Apache Airflow or Prefect to schedule and monitor tasks, adding retries, alerting, and back‑pressure controls. For the load phase, I employ bulk inserts with partitioning and indexing to optimize performance, and I maintain a separate audit table to track lineage and provenance. Throughout the pipeline, I implement data quality checks (null checks, range checks, referential integrity) and publish metrics to a monitoring dashboard. This architecture supports both batch and real‑time workloads, scales horizontally by adding compute nodes, and provides clear visibility into failures, enabling rapid remediation.