Sr. Associate, Data Quality Engineering

What You Will Do:

• Design, develop, and maintain automated data quality checks, validation rules, and exception-handling pipelines using Python.
• Implement and maintain data quality frameworks aligned with DAMA-DMBOK and internal governance standards.
• Develop and implement data quality metrics, scorecards, and dashboards to track completeness, accuracy, consistency, timeliness, and validity across reinsurance datasets.
• Build and maintain data quality monitoring pipelines that integrate with existing data infrastructure (e.g., data lake, data warehouse, ETL/ELT workflows).
• Collaborate with data stewards, data owners, and business stakeholders to define acceptable quality thresholds and remediation workflows.
• Investigate root causes of data quality issues across investment data, treaty data, claims data, and exposure datasets; document findings and drive resolution.
• Partner with data engineering teams to integrate quality gates into CI/CD and data pipeline processes.
• Maintain comprehensive documentation of quality rules, lineage, and remediation outcomes within the data governance catalog.
• Support internal and external audits by providing data quality evidence and lineage reports.

What You Will Have:

• 3–6 years of experience in a data quality, data engineering, or analytics engineering role.