Data Quality and Governance Questions

Covers the principles, frameworks, practices, and tooling used to ensure data is accurate, complete, timely, and trustworthy across systems and pipelines. Key areas include data quality checks and monitoring such as nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes design of service level agreements for data freshness and accuracy, data lineage and impact analysis, metadata and catalog management, data classification, access controls, and compliance policies. Encompasses operational reliability of data systems including failure handling, recovery time objectives, backup and disaster recovery strategies, observability and incident response for data anomalies. Also covers domain and system specific considerations such as customer relationship management and sales systems: common causes of data problems, prevention strategies like input validation rules, canonicalization, deduplication and training, and business impact on forecasting and operations. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship, and implementing automated pipelines and governance controls.