Senior Associate, Data Quality Engineering

Fortitude Re

Data Quality Engineer responsible for designing and maintaining data quality frameworks at Fortitude Re. Collaborating with teams to ensure data accuracy and compliance across lifecycle processes.

Posted 6/1/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $120,000 - $130,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudETLGoogle Cloud PlatformPandasPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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)
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

Benefits

Comp & perks

Annual bonus based on company and individual performance
Generous benefits package

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Hard Skills & Tools

PythonSQLdata quality checksdata quality frameworksdata quality metricsdata quality monitoring pipelinesETLELTdata processingpipeline automation

Soft Skills

analytical skillsproblem-solving skillsattention to detailcommunication skills