Job Description

Summary: We are seeking an experienced MDM Data Quality Analyst to support enterprise Master Data Management (MDM) initiatives focused on improving data quality, governance, and analytics across multiple business domains. This role is responsible for performing data quality analysis, data profiling, business and technical requirements gathering, testing, validation, and governance activities to support the creation and maintenance of a trusted enterprise master data platform. The ideal candidate will work closely with technical leads, business stakeholders, data engineers, and development teams to ensure accurate, consistent, and reliable master data while supporting data governance initiatives and enterprise-wide data quality programs.

Key Responsibilities

• Develop and maintain complex SQL queries to support MDM solutions and data analysis.
• Perform data profiling, data quality assessments, validation, reconciliation, and root cause analysis.
• Support SQL analysis, reporting, and data validation activities for enterprise MDM solutions.
• Collaborate with project teams to support database objects including tables, views, stored procedures, and related data structures.
• Execute scripts and assist with development, maintenance, and modification of existing data processes.
• Participate in unit testing, integration testing, system testing, and production validation activities.
• Gather, analyze, and document business and technical requirements.
• Support User Acceptance Testing (UAT) by assisting business users, validating results, and documenting issues.
• Analyze data anomalies, matching issues, duplicate records, and master data quality concerns.
• Support data governance, stewardship, and metadata management initiatives.
• Prepare documentation, reports, and recommendations for data quality improvements and remediation activities.
• Participate in project discussions and recommend technical or business solutions that improve data integrity and operational efficiency.
• Collaborate with business users, developers, QA teams, and architects throughout the Software Development Life Cycle (SDLC).
• Support enterprise reporting, analytics, and business intelligence initiatives through high-quality master data.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or equivalent experience.
• 5+ years of experience in Data Analysis, Data Quality, Master Data Management (MDM), Business Analysis, or Data Governance.
• Hands-on experience working with Informatica MDM or similar enterprise MDM platforms.
• Strong SQL skills with experience working on large relational databases.
• Experience performing:
  • Data Profiling
  • Data Validation
  • Data Reconciliation
  • Data Quality Analysis
  • Data Matching
  • Duplicate Detection
  • Record Standardization
• Experience gathering and documenting business and technical requirements.
• Experience supporting QA, UAT, System Testing, and Production Validation.
• Strong understanding of:
  • Master Data Management
  • Data Governance
  • Data Stewardship
  • Data Quality Frameworks
  • Unique Customer/Member Identifier concepts
• Excellent analytical, troubleshooting, and problem-solving skills.
• Experience working within Agile or SDLC project environments.

Preferred Qualifications

• Experience with additional enterprise or cloud-native MDM platforms.
• Experience supporting healthcare, insurance, public sector, government, Medicaid, claims, eligibility, provider, or customer master data.
• Experience working in regulated environments.
• Experience with Agile/Scrum delivery methodologies.
• Exposure to Python scripting for data validation, automation, or profiling.
• Experience with interoperability formats including:
  • XML
  • JSON
  • REST APIs
  • EDI/X12
  • HL7/FHIR
• Exposure to modern cloud data platforms such as:
  • Microsoft Azure
  • Azure Data Factory (ADF)
  • Azure Data Lake
  • Databricks
  • Snowflake
• Experience supporting cloud migration, MDM modernization, EDW, or ETL modernization initiatives.
• Familiarity with AI/ML, Generative AI, or LLM-assisted development, testing, documentation, or data quality processes.

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