- Analyze, manage, and reconcile product master data across multiple source systems, MDM platforms,
and downstream applications.
- Leverage AI/GenAI tools to accelerate data profiling, analysis, data quality rule development,
documentation, issue investigation, and root-cause analysis.
- Translate business and product requirements into clear data requirements, attribute definitions,
validation rules, and analytical specifications.
- Perform detailed data profiling to identify quality issues, gaps, inconsistencies, anomalies, duplicates,
and data integrity risks across product attributes and relationships.
- Define, document, implement, and validate data quality rules covering completeness, accuracy,
validity, consistency, uniqueness, and business-rule compliance.
- Develop source-to-target analysis and reconciliation approaches to validate data movement,
mappings, transformations, and synchronization across systems.
- Work with Product and Business stakeholders to understand product structures, SKUs,
product/component relationships, configurations, classifications, and product lifecycle requirements.
- Analyze and resolve complex product data issues, including incorrect attribute values, missing data,
duplicate records, mapping issues, hierarchy discrepancies, and configuration gaps.
- Support MDM data onboarding, enrichment, cleansing, migration, and transformation activities,
including data preparation and validation.
- Develop data validation logic and partner with technical teams on implementation, testing, and
automation of DQ rules.
- Participate in UAT, data certification, defect triage, root-cause analysis, and issue resolution; ensure
defects are clearly documented and tracked through closure.
- Create data correction files and coordinate with technical teams to implement and validate required
fixes.
- Support development and maintenance of product data dictionaries, attribute definitions, business
rules, reference data, and data governance documentation.
- Partner with Data Governance teams to define data ownership, standards, allowed values, reference
data, and validation requirements.
- Monitor and report data quality metrics, trends, defects, and remediation progress, providing clear
status updates and recommendations to stakeholders.
- Identify recurring data quality issues and recommend process, data model, integration, or system
improvements to prevent recurrence.
- Contribute to large-scale MDM implementation, migration, and transformation programs and support
continuous improvement of product data processes.