About AAIS
Since 1936, American Association of Insurance Services (“AAIS”) has served the property and casualty insurance industry as the only national not-for-profit advisory organization governed by its member companies. We are committed to evolving with changes in the insurance industry, adding value to our members, regulators, and the industry with responsive products and insights.
Purpose
The MDM Data Engineer supports AAIS’s Master Data Management platform, data governance frameworks, and supporting data integration infrastructure. This role is the operational and technical backbone of our MDM practice, owning the full lifecycle of master data — from ingestion and standardization through survivorship, stewardship, and delivery.
The ideal candidate brings deep, hands-on MDM expertise paired with strong data engineering fundamentals. They thrive in a collaborative, cross-functional environment and are comfortable translating complex data governance concepts into practical, scalable solutions.
Responsibilities
Master Data Architecture & Development
- 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.
Data Governance & Stewardship
- 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.
Data Quality & Profiling
- 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.
ETL/ELT & Integration Engineering
- 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.
Broader Data Engineering Responsibilities
- 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.
- Clearly communicate technical concepts, data quality findings, and governance recommendations to non-technical stakeholders and leadership.
- Perform additional duties as assigned or requested.
Knowledge, Skills, and Abilities
- Deep, hands-on expertise in MDM platforms and patterns (e.g., Informatica MDM, Reltio, Semarchy, or equivalent) — including match/merge configuration, survivorship rules, and workflow design.
- Strong data modeling skills with experience in both MDM-specific models and traditional warehouse patterns (Kimball, Inmon, Data Vault).
- Proficiency in SQL and experience working with large structured and semi-structured datasets.
- Experience designing and implementing data governance frameworks, including stewardship workflows, data quality rules, and lineage tracking.
- Hands-on experience with cloud data platforms, preferably AWS (Glue, Athena, S3, RDS).
- Familiarity with ETL/ELT tooling such as AWS Glue.
- Ability to operate independently as a developer while collaborating effectively across technical and business teams.
- Excellent written and oral communication skills; ability to articulate data governance and MDM concepts to non-technical audiences.
- Strong critical thinking and problem-solving skills with the ability to manage multiple priorities and meet deadlines.