Data Quality and Database Management Questions

Principles and practices for ensuring clean, accurate, and well governed databases and data systems. Covers data hygiene techniques such as deduplication, validation rules, field standardization, regular audits, record merging, archival policies, and remediation workflows. Includes data governance topics like data ownership, stewardship, policy definition, documentation, privacy and compliance controls, and role based access. Addresses how poor data quality propagates downstream into reporting, analytics, personalization, and business decision making, and how to trace root causes across ingestion, transformation, and storage layers. Candidates should be able to diagnose common integrity issues (duplicates, stale or missing fields, schema drift, broken foreign keys), propose tooling and process solutions, and explain how to operationalize data quality and governance at scale across an organization's data systems.