Overview:
We are seeking an experienced HR Data Analyst to support a large-scale enterprise IT implementation, focusing on HR data analysis, cleansing, mapping, validation, and migration readiness. The ideal candidate will have a strong background in data quality management, HR systems, and data migration processes, with the ability to identify inconsistencies, resolve data discrepancies, and ensure accurate data transformation.
In this role, you will collaborate closely with HR subject-matter experts (SMEs), IT teams, project managers, and other stakeholders to ensure HR data is accurate, complete, and properly structured for migration into a new enterprise system. You will also support user acceptance testing (UAT), data reconciliation, reporting, and documentation to ensure successful system implementation.
Responsibilities:
- Analyze and assess HR datasets to identify data gaps, inconsistencies, duplicates, and quality issues.
- Perform data cleansing, normalization, and validation activities to ensure data accuracy, completeness, and consistency.
- Develop and maintain data quality standards, business rules, validation checklists, and supporting documentation.
- Identify data discrepancies and recommend corrective actions to improve overall data integrity.
- Monitor data quality trends and track remediation efforts throughout the implementation lifecycle.
- Support HR data mapping, transformation, and migration activities for a new enterprise IT system.
- Prepare, organize, and validate structured data inputs for migration and conversion cycles.
- Collaborate with HR SMEs and technical teams to define data requirements, validate transformation logic, and resolve migration-related issues.
- Review mock conversion outputs to verify data accuracy, completeness, and alignment with business requirements.
- Perform data reconciliation between legacy and target systems to identify and resolve discrepancies.
- Assist with troubleshooting data migration defects and implementing corrective actions.
- Support UAT activities to validate HR data accuracy and system functionality.
- Develop and execute data validation scenarios based on business requirements and expected system behavior.
- Identify, document, and track data-related defects discovered during testing.
- Collaborate with functional and technical teams to resolve issues and verify corrections.
- Ensure migrated data meets established quality standards and business requirements before implementation.
- Develop and maintain data quality reports, issue trackers, reconciliation summaries, and project status documentation.
- Document data mapping rules, transformation requirements, validation procedures, and cleanup processes.
- Prepare reports highlighting data quality trends, outstanding issues, and remediation progress.
- Communicate findings, risks, and recommendations to project managers and stakeholders.
- Maintain accurate documentation to support project transparency, auditability, and knowledge transfer.
Qualifications:
- Active TS/SCI with Polygraph
- Bachelor's degree in Information Systems, Business Administration, Human Resources, Data Analytics, or a related field, or 4+ years of equivalent professional experience.
- 3–5 years of experience in data analysis, HR systems, HR operations, or a related discipline.
- Demonstrated experience performing data cleansing, validation, reconciliation, or migration activities.
- Strong understanding of data quality management, data mapping, and transformation processes.
- Experience identifying and resolving data inconsistencies, discrepancies, and quality issues.
- Ability to analyze complex datasets and translate business requirements into actionable data solutions.
- Strong analytical, organizational, and problem-solving skills with exceptional attention to detail.
- Excellent written and verbal communication skills, including experience preparing technical documentation and reports.
- Ability to collaborate effectively with HR SMEs, technical teams, and project stakeholders.
- Demonstrated ability to work independently, manage competing priorities, and meet project deadlines.
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