staff-data-engineer

To build financial data pipelines using medallion architecture, you design layered workflows progressing from Bronze to Silver to Gold. This structures data systematically from raw ingestion to refined reporting datasets.

Orchestrating ETL workflows in regulated environments is best achieved using Airflow DAGs with dependencies, retries, and observability. This ensures compliant data movement, enforces idempotency, and maintains lineage.

Schema evolution impacts ETL pipelines by requiring validation during data ingestion. Managing schema evolution in enterprise data systems ensures source structure changes do not break downstream processing or reporting dashboards.

Yes, you can use Airflow and dbt together for financial data ingestion. This combination orchestrates DAGs for movement while leveraging dbt for transformations, enforcing idempotency and cost-awareness in pipeline design.

ETL pipelines need idempotency and lineage in financial data processing to guarantee reliable retries and full traceability. Enforcing these properties ensures data accuracy, compliance, and auditability across complex enterprise workflows.