Data Quality Analytics Engineer

Kforce has a client in Miami, FL that is seeking a Data Quality Analytics Engineer to join their team. Summary: In this role, you will design and operationalize enterprise-grade data quality and MDM frameworks, extend them into how sensitive data is classified and protected, and build the analytics that tell the organization how healthy its data actually is. You will also get to work at the front edge of applied AI - using AWS Bedrock, agentic AI patterns, and MCP connections to automate data trust monitoring and remediation at a scale no manual process can reach. We are seeking someone who likes solving problems that are equal parts of engineering, analytics, and detective work. 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
• Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field; Equivalent practical experience will be considered
• 5-7 years of experience in analytics, data engineering, data quality, or data management, including hands-on delivery on analytics projects and exposure to data quality, master data management, and/or data protection initiatives
• Experience defining data quality metrics and SLAs and reporting on data health to senior stakeholders
• Working knowledge of core data quality concepts: profiling, rule authoring, exception management, reconciliation, and quality metrics
• Understanding of master data management fundamentals - matching, survivorship, golden records, and hierarchy management
• Solid understanding of data governance principles, data cataloging, metadata management, and data lineage
• Strong proficiency in SQL and Python for data analysis, quality rule development, and reconciliation across relational and cloud-native platforms
• A genuine proponent of data quality and data trust practices - someone who argues for the right fix rather than the fast one, and can explain why it matters to a non-technical audience
• Awareness of data protection concepts: sensitive data classification, masking, least-privilege access, and the regulatory drivers behind them
• Strong adherence to software engineering best practices including version control (git), modular code design, Agile methodologies, and CI/CD pipelines
• Curiosity and speed in learning emerging data quality and AI technologies, and comfort adapting to an evolving enterprise data landscape
• Demonstrated ability to turn data into analytics people act on - dashboards, scorecards, or reporting products with a real audience
• Ability to work collaboratively with business users, data stewards, and technical teams to gather requirements and deliver solutions