Data Quality Analytics Engineer

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. If you like solving problems that are equal parts engineering, analytics, and detective work - and you care about getting the answer right - this is a good seat.

• 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 \n

• Strong proficiency in SQL and Python for data analysis, quality rule development, and reconciliation across relational and cloud-native platforms \n

• 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 \n

• Working knowledge of core data quality concepts: profiling, rule authoring, exception management, reconciliation, and quality metrics \n

• Understanding of master data management fundamentals - matching, survivorship, golden records, and hierarchy management \n

• Awareness of data protection concepts: sensitive data classification, masking, least-privilege access, and the regulatory drivers behind them \n

• Demonstrated ability to turn data into analytics people act on - dashboards, scorecards, or reporting products with a real audience \n

• Solid understanding of data governance principles, data cataloging, metadata management, and data lineage \n

• Experience defining data quality metrics and SLAs and reporting on data health to senior stakeholders \n

• Ability to work collaboratively with business users, data stewards, and technical teams to gather requirements and deliver solutions \n

• Strong adherence to software engineering best practices including version control (git), modular code design, Agile methodologies, and CI/CD pipelines \n

• Curiosity and speed in learning emerging data quality and AI technologies, and comfort adapting to an evolving enterprise data landscape \n

• Bachelor’s degree in computer science, Information Systems, Data Science, or a related field; equivalent practical experience will be considered \n, * Hands-on experience with a data quality platform such as Atacama ONE or Ataccama DQ for data profiling, quality rule authoring, and workflow management \n

• Demonstrated experience implementing or managing Master Data Management solutions - Reltio strongly preferred \n

• Exposure to data protection or data security tooling such as Varonis, including sensitive data discovery and access analytics \n

• Proficiency with Snowflake, including data quality patterns, dynamic data masking, and Snowflake’s native data quality features \n

• Experience working in AWS cloud environments and with services relevant to data quality and governance (S3, Glue, Lambda, Step Functions) \n

• Ability to build and use MCP (Model Context Protocol) connections to wire AI agents into enterprise data and tooling \n

• Familiarity with agentic AI on AWS Bedrock, and experience with AI development assistants such as Claude Code or Codex, applied to automating data operations workflows \n

• Experience working with regulated or privacy-sensitive data domains \n

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• Design and operationalize end-to-end data quality frameworks - profiling, cleansing, validation, and continuous monitoring - across the enterprise data estate \n

• 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 \n

• 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 \n

• 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 \n

• Deploy and administer data quality and MDM platforms (for example Atacama ONE and Reltio) to enforce quality rules, lineage tracking, and issue resolution workflows \n

• 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 \n

• Develop and maintain SQL- and Python-based data quality rules, reconciliation logic, and automated validation across structured and semi-structured sources \n

• Build and maintain data quality pipelines in Snowflake and AWS cloud environments, leveraging native features for scalable quality checks and anomaly detection \n

• 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 \n

• Define and track data quality KPIs and SLAs; report data health clearly to both business and technology stakeholders \n

• Lead data quality issue triage, root cause analysis, and remediation in collaboration with upstream data owners and platform teams \n

• Partner with data governance, data engineering, and business teams to establish enterprise data standards, taxonomies, and ontologies \n

• Support CI/CD practices for data quality rule deployment, version control, and automated regression testing \n

• Contribute to data quality, MDM, and data protection policies, standards, and best-practice documentation \n

• Champion a culture of data trust across the organization through training, evangelism, and hands-on enablement of data consumers and producers \n

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