Data Quality Analytics Engineer

Overview In this role you design and operationalize enterprise-grade data quality and MDM frameworks, extending them to data classification and protection. You’ll build analytics to reveal data health and support decision-making at scale, using frontier AI and automation. You’ll work at the intersection of engineering, analytics, and detective work to improve data trust. This position offers exposure to modern data platforms and security-focused data stewardship in a dynamic enterprise setting. ResponsibilitiesDesign and operationalize end-to-end data quality frameworks (profiling, cleansing, validation, monitoring) across the enterprise data estateCreate data quality scorecards and dashboards to communicate data health to business and leadershipCollaborate with analytics/BI to translate use cases into data quality requirementsConfigure and manage Master Data Management (MDM) solutions to maintain golden recordsDeploy and administer data quality and MDM platforms (e.g., Ataccama ONE, Reltio) with lineage and issue workflowsExtend data quality practices to data protection via sensitive data discovery, masking, and access monitoring with tools like VaronisDevelop SQL- and Python-based data quality rules and automated validation across structured and semi-structured sourcesEnsure adherence to software engineering practices (version control, Agile, CI/CD) and partner with security/privacy teams on governanceTurn data into actionable analytics products (dashboards, scorecards) for real audiencesWork with business users, data stewards, and tech teams to gather requirements and deliver solutions Key requirementsBachelor's degree in Computer Science, Information Systems, Data Science, or related field5-7 years in analytics, data engineering, data quality, or data management with practical project deliveryExperience defining data quality metrics, SLAs, and reporting to senior stakeholdersKnowledge of core data quality concepts: profiling, rule authoring, exception management, reconciliation, quality metricsUnderstanding of MDM fundamentals: matching, survivorship, golden records, hierarchy managementStrong SQL and Python for data analysis and rule developmentAwareness of data protection concepts: sensitive data classification, masking, least-privilege accessExperience with data governance, data cataloging, metadata management, and data lineageDemonstrated ability to deliver analytics products used by non-technical audiencesCollaborative, curious, and able to adapt to evolving enterprise data landscapecollaborationcuriosityability to explain technical concepts to non-technical audiencesSQLPythondata quality concepts