How do you approach data quality, governance and documentation in a data product team?

08 July 2026

Question

How would you demonstrate an understanding of accurate, trusted and well-documented data products within an organisation?

I would treat data quality, governance and documentation as part of the engineering work, not as admin added afterwards.

Data Quality: I would define checks for completeness, validity, uniqueness and consistency. Examples include required-field checks, duplicate detection, schema validation and reconciliation against source counts. - Preventing Bad Data Propagation: Quality checks should run as part of the pipeline so issues are caught before incorrect data reaches downstream users. - Governance: I would follow agreed naming standards, access controls, retention rules and metadata conventions. In a housing organisation this is especially important because resident and operational data can be sensitive. - Documentation: I would keep data dictionaries, field definitions, business rules and lineage records up to date so others can trust and reuse the data. - Ownership and Trust: Good data products should have clear owners, known refresh frequency and well-defined intended use. - Continuous Improvement: When an issue is found, I would fix the root cause and update the check or documentation so the same problem is less likely to recur. In interview terms, I would summarise this as: trusted data comes from repeatable engineering controls, clear definitions and strong discipline around documentation.