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: nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes designing 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: failure handling, recovery time objectives, backup and disaster recovery strategies, data observability, and incident response for data anomalies. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship (data owner, steward, custodian), building golden-record and master-data-management strategies for record linkage and deduplication across source systems (illustrative domains include CRM and sales data, IoT telemetry, financial transactions, and event or log data, among others), and implementing automated pipelines and governance controls.