Data Quality and Master Data Management Engineer responsible for MDM and DQ solutions at Sinclair, Inc. Ensuring data accuracy, consistency, and governance across enterprise systems.
Responsibilities
Design, develop, and maintain Master Data Management (MDM) and Data Quality (DQ) solutions
Own, maintain, and expand the ED&I-built Data Quality Foundation
Translate data governance policies, quality standards, and field-level requirements into technical data quality rules
Build, maintain, and support scalable pipelines that detect, measure, and store data quality results across multiple domains
Implement automated frameworks that surface issues in real-time
Analyze and map source system attributes to the Golden Record structure
Create and manage transformation logic to standardize, harmonize, and prepare data for MDM use cases
Maintain and enhance the Data Quality Dashboard
Ensure dashboards reflect clear, executive-ready views of data quality trends
Manage end-to-end exception reporting
Partner with the Enterprise Data Governance Office
Translate governance requirements into actionable technical specifications
Act as a trusted technical advisor to Data Stewards, Data Owners, and Data Council representatives
Monitor performance of Snowflake workloads
Maintain documentation of rules, mappings, logic, lineage, and dashboard changes
Requirements
5 or more years of experience in Data Engineering, Data Quality, or Master Data Management roles
Strong hands-on experience implementing and managing MDM and DQ frameworks (commercial or open source)
Advanced proficiency with Snowflake, including schema design, SQL optimization, and ELT orchestration
Strong programming skills in SQL and Python for data validation, profiling, and automation
Experience building reporting or dashboard layers (Cognos Analytics, SSRS, Power BI, Tableau, or similar)
Familiarity with MDM concepts such as golden record design, survivorship, match/merge logic, and hierarchy management
Understanding of data quality dimensions, measurement techniques, score carding, and exception management
Experience with metadata-driven design, rule frameworks, and repeatable automation patterns
Deep understanding of data governance principles, including metadata, reference data, and stewardship processes
Proven experience integrating data management solutions with ERP, CRM, and analytics platforms
Familiarity with AI/ML-based approaches to improve data quality and anomaly detection
Experience with AWS or Azure data services and modern CI/CD practices for data pipeline deployment
Strong analytical and troubleshooting skills, with the ability to operate effectively in complex data ecosystems.
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