Data Quality Analytics Engineer

Job Description

This position is a contract to hire. Local candidates only as interviews are onsite.

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The Data Quality Analytics Engineer sits at the intersection of four disciplines most companies keep in separate silos: data quality, master data management (MDM), data protection, and analytics. This position is part of the Data & AI team, which exists to turn data into better business outcomes and weave those insights into the company’s corporate fabric. 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.

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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.

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Responsibilities

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• Design and operationalize end-to-end data quality frameworks — profiling, cleansing, validation, and continuous monitoring — across the enterprise data estate
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• 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
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• 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
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• 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
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• Deploy and administer data quality and MDM platforms (for example Atacama ONE and Reltio) to enforce quality rules, lineage tracking, and issue resolution workflows
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• 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
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• Develop and maintain SQL- and Python-based data quality rules, reconciliation logic, and automated validation across structured and semi-structured sources
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• Build and maintain data quality pipelines in Snowflake and AWS cloud environments, leveraging native features for scalable quality checks and anomaly detection
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• 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
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• Define and track data quality KPIs and SLAs; report data health clearly to both business and technology stakeholders
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• Lead data quality issue triage, root cause analysis, and remediation in collaboration with upstream data owners and platform teams
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• Partner with data governance, data engineering, and business teams to establish enterprise data standards, taxonomies, and ontologies
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• Support CI/CD practices for data quality rule deployment, version control, and automated regression testing
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• Contribute to data quality, MDM, and data protection policies, standards, and best-practice documentation
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• Champion a culture of data trust across the organization through training, evangelism, and hands-on enablement of data consumers and producers
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Required Qualifications

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• 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
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• Strong proficiency in SQL and Python for data analysis, quality rule development, and reconciliation across relational and cloud-native platforms
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• 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
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• Working knowledge of core data quality concepts: profiling, rule authoring, exception management, reconciliation, and quality metrics
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• Understanding of master data management fundamentals — matching, survivorship, golden records, and hierarchy management
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• Awareness of data protection concepts: sensitive data classification, masking, least-privilege access, and the regulatory drivers behind them
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• Demonstrated ability to turn data into analytics people act on — dashboards, scorecards, or reporting products with a real audience
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• Solid understanding of data governance principles, data cataloging, metadata management, and data lineage
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• Experience defining data quality metrics and SLAs and reporting on data health to senior stakeholders
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• Ability to work collaboratively with business users, data stewards, and technical teams to gather requirements and deliver solutions
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• Strong adherence to software engineering best practices including version control (git), modular code design, Agile methodologies, and CI/CD pipelines
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• Curiosity and speed in learning emerging data quality and AI technologies, and comfort adapting to an evolving enterprise data landscape
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• Bachelor’s degree in computer science, Information Systems, Data Science, or a related field; equivalent practical experience will be considered
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Preferred Qualifications

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• Hands-on experience with a data quality platform such as Atacama ONE or Ataccama DQ for data profiling, quality rule authoring, and workflow management
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• Demonstrated experience implementing or managing Master Data Management solutions — Reltio strongly preferred
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• Exposure to data protection or data security tooling such as Varonis, including sensitive data discovery and access analytics
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• Proficiency with Snowflake, including data quality patterns, dynamic data masking, and Snowflake’s native data quality features
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• Experience working in AWS cloud environments and with services relevant to data quality and governance (S3, Glue, Lambda, Step Functions)
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• Ability to build and use MCP (Model Context Protocol) connections to wire AI agents into enterprise data and tooling
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• 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
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• Experience working with regulated or privacy-sensitive data domains
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