The Data Quality Engineer is a mid-level technical role within the Data Governance team, responsible for designing, building, and maintaining automated data quality frameworks. The role partners closely with data engineers, actuaries, and business analysts to embed quality controls across the data lifecycle, from ingestion through to reporting, ensuring that risk, claims, and treaty data meet the accuracy and completeness standards required for regulatory compliance and sound decision-making.

The position reports to the VP, Data Governance.

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.

• Experience in reinsurance, insurance, or financial services preferred.

• Hands-on experience with SQL and working in cloud-based data environments.

• Experience with cloud platforms such as AWS, Azure, or GCP preferred.

• Strong proficiency in Python for data processing, quality rule development, and pipeline automation (e.g., pandas, Great Expectations, polars) preferred.

• Familiarity with data governance principles and frameworks (DAMA-DMBOK, DCAM, or equivalent).

• Experience working within structured data environments in financial services, insurance, or reinsurance.

• Takes ownership of quality outcomes and delivers solutions end-to-end under limited supervision; identifies gaps before they become issues.

• Strong analytical and problem-solving skills with a meticulous attention to detail.

• Ability to communicate technical data quality concepts clearly to non-technical business stakeholders.

• Understanding of data controls supporting audit, compliance, and reporting requirements preferred.