Unreliable data is a direct P&L risk in quantitative trading. Missing values, schema drift, or statistical outliers in price feeds, alternative data, or earnings transcripts corrupt feature engineering and lead to flawed signal generation. This workflow automates continuous validation, checking for completeness, distribution shifts, and cross-source consistency. It eliminates manual spot-checking, reduces model degradation from silent data corruption, and ensures trading decisions are based on verified inputs. The architecture must integrate with data lakes (Snowflake, Databricks), orchestration (Apache Airflow, Prefect), and observability platforms (Datadog, Grafana) to be production-grade.




