Master Data Management Data Engineer

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AAIS (American Association of Insurance Services)

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1936

💼 Consulting

⚖️ Legal

📦 Logistics

Consulting • Legal • Logistics

AAIS (American Association of Insurance Services) is a member-governed, not-for-profit advisory organization founded in 1936 that provides products and services to property & casualty insurance carriers across the United States. AAIS produces insurance forms, manuals, rating guidance and delivers regulatory compliance support, onboarding, actuarial services, statistical data management and data/technology toolkits (e. g. , FLAMES, Speed to Market, openIDL) to help carriers manage filings, reporting and speed-to-market. The organization focuses on serving member carriers, regulators and partners with advisory solutions, data services and educational resources to support compliant, data-driven insurance operations.

📋 Description

• Build and maintain the MDM platform, including entity resolution, match/merge rules, survivorship logic, and golden record management. • Develop and enforce data models that support party, policy, and reference data domains across the enterprise. • Architect MDM hub configurations (registry, consolidation, or co-existence models) appropriate to AAIS’s operational context. • Build and maintain MDM integration layers connecting source systems, the data lake/warehouse, and downstream consumers. • Define and implement data governance policies, standards, and workflows in collaboration with Data Stewards and the Director of Data Solutions. • Develop data quality rules, profiling routines, and exception-handling workflows to ensure master data integrity across all jurisdictions. • Maintain business glossaries, data dictionaries, and lineage documentation for all master data domains. • Partner with business stakeholders to define data ownership, stewardship responsibilities, and escalation paths for data quality issues. • Analyze and profile source data to assess quality, completeness, atomicity, and referential integrity prior to MDM onboarding. • Implement automated data quality monitoring and alerting to proactively surface master data anomalies and support data quality KPI tracking. • Establish and track data quality KPIs and SLAs for key master data domains. • Design and develop data integration pipelines that feed the MDM platform from disparate source systems in batch and near-real-time. • Build and maintain ETL/ELT workflows using cloud-native tooling (AWS Glue, Step Functions) and integration platforms. • Ensure referential integrity and consistent application of master data identifiers across the data ecosystem. • Support the broader Data Lake/Warehouse environment, contributing to data modeling, analytics engineering, and BI delivery as needed. • Collaborate with the Data Engineering team to ensure MDM outputs are properly integrated into reporting, analytics, and statistical data collection workflows. • Contribute to Agile sprint planning, technical documentation, and peer code review processes.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Management, or a related discipline. • 5+ years of data engineering experience, with a minimum of 3 years focused specifically on MDM platform development and data governance. • Demonstrated experience owning MDM implementations end-to-end, including data model design, match/merge tuning, and integration delivery. • Experience with data quality tooling and profiling methodologies. • Proficiency in SQL & Python. • Experience in property and casualty insurance or other regulated industries a plus. • Bonus: Experience working with statistical data, Actuarial models, or licensed statistical agent environments.

🏖️ Benefits

• Up to 5% travel for annual company gatherings or team sessions