Data Quality Analytics Engineer

W2 Role... No C2C

Hybrid – Irving, TX or Miami, FL (3x/week onsite)

Principal Duties and Responsibilities:

• Design and operationalize end-to-end data quality frameworks — profiling, cleansing, validation, and continuous monitoring — across the enterprise data estate
• Make data trust visible through analytics: build data quality scorecards, executive dashboards, and self-service views that show business teams the health of the data behind their decisions
• Partner with analytics and BI teams to translate analytics use cases into data quality requirements, so trust is designed in upstream rather than patched downstream
• Configure and manage Master Data Management (MDM) solutions to create and maintain golden records for critical business entities such as customers, products, vendors, and properties
• Deploy and administer data quality and MDM platforms (for example Ataccama ONE and Reltio) to enforce quality rules, lineage tracking, and issue resolution workflows
• Extend the data quality practice into data protection — partner with security and privacy teams on sensitive data discovery, classification, masking, and access monitoring using tools such as Varonis
• Develop and maintain SQL- and Python-based data quality rules, reconciliation logic, and automated validation across structured and semi-structured sources
• Build and maintain data quality pipelines in Snowflake and AWS cloud environments, leveraging native features for scalable quality checks and anomaly detection
• Prototype and productionize agentic AI workflows — using AWS Bedrock, MCP connections, and modern AI development tools — to automate profiling, issue triage, root cause analysis, and self-healing remediation
• Define and track data quality KPIs and SLAs; report data health clearly to both business and technology stakeholders
• Lead data quality issue triage, root cause analysis, and remediation in collaboration with upstream data owners and platform teams
• Partner with data governance, data engineering, and business teams to establish enterprise data standards, taxonomies, and ontologies
• Support CI/CD practices for data quality rule deployment, version control, and automated regression testing
• Contribute to data quality, MDM, and data protection policies, standards, and best-practice documentation
• Champion a culture of data trust across the organization through training, evangelism, and hands-on enablement of data consumers and producers