Clear communication and stakeholder management (aligning different teams on one set of definitions)
Process design and documentation (workflows, ownership, decision rights)
Analytical problem-solving (finding root causes of data issues and prioritizing fixes)
SQL and data querying (validating records, profiling data, investigating defects)
Data quality methods (rules, scoring, monitoring, issue management)
Master data concepts (golden record, matching/merging, hierarchy management)
Metadata management (data dictionary, business glossary, lineage basics)
Data modeling fundamentals (entities, attributes, relationships)
Privacy and compliance awareness (handling sensitive data appropriately)
Tool familiarity (common MDM, data catalog, and data quality platforms)