data quality specialist

12 hours ago

Manila, National Capital Region, Philippines CARE USA Full time $104,000 - $130,878 per year

JOB SUMMARY

The Data Quality Specialist will support CARE's global Oracle implementation by ensuring the accuracy, consistency, and readiness of data across Core HR, Supply Chain Management (SCM), and Enterprise Resource Planning (ERP) systems. This role will focus on identifying and resolving data inconsistencies, such as formatting issues, across data extracts from legacy system. The Specialist will collaborate with functional experts and project team members, including business analysts and change managers, to extract, clean, and prepare data for migration. They will also contribute to process improvement efforts and lessons learned to support future implementation phases. 

This position is currently funded through 30 June 2026 but is planned to continue through August 2027, subject to availability of funding.

RESPONSIBILITIES

Data Cleansing and Standardization

  • Identify inconsistencies in legacy data (e.g., phone numbers and addresses) and apply appropriate methods to clean and standardize it for Oracle compatibility. Use Excel formulas, scripts, or manual review as needed. This ensures high-quality data is migrated into Oracle systems, reducing errors and rework. Work with offices to help extract data from legacy systems.

Data Validation and Migration Support

  • Collaborate with technical teams to validate data against Oracle requirements. Assist in test uploads and troubleshoot data-related issues during migration cycles. This helps ensure a smooth transition and system integrity.

Process Documentation and Feedback

  • Document data issues, fixes, and lessons learned. Provide feedback to improve upstream data entry processes and support better data governance. This contributes to long-term data quality and sustainability.

Cross-Functional Collaboration

  • Work closely with HR, Finance, and Supply Chain teams, as well as project managers and change managers, to align on data needs and priorities. This ensures that data work is integrated into the broader implementation effort.

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