Sr. Associate, Data Quality Engineering

Data Quality Engineer

About

Fortitude Re is a leading provider of legacy reinsurance solutions, working with insurance companies to execute transformational solutions for legacy Life & Annuity and P&C lines. They manage approximately $111 billion across various insurance products and are backed by sophisticated institutional investors. Fortitude Re focuses on a long-term view of growth and innovation in the global insurance industry.

Responsibilities

• 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.

Requirements

• 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.
• 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.