Data Quality Analytics Engineer (Miami)

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

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.

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.

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 Atacama 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

Required Qualifications

• 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
• 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
• 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
• Awareness of data protection concepts: sensitive data classification, masking, least-privilege access, and the regulatory drivers behind them
• Demonstrated ability to turn data into analytics people act on — dashboards, scorecards, or reporting products with a real audience
• Solid understanding of data governance principles, data cataloging, metadata management, and data lineage
• Experience defining data quality metrics and SLAs and reporting on data health to senior stakeholders
• Ability to work collaboratively with business users, data stewards, and technical teams to gather requirements and deliver solutions
• 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
• Bachelor’s degree in computer science, Information Systems, Data Science, or a related field; equivalent practical experience will be considered

Preferred Qualifications

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