Data Quality Analyst – Data Governance, AI-Ready Data

Role Overview

• Implement and operate data quality controls (profiling, validation, reconciliation) in accordance with governance defined standards and thresholds.
• Measure and monitor data quality dimensions including accuracy, completeness, consistency, timeliness, and fitness for use.
• Produce data quality KPIs and metrics required for MR 5f governance reporting and dashboards.
• Apply AI assisted data quality capabilities (e.g., automated profiling, anomaly detection, rule generation) to improve coverage, efficiency, and early detection of data quality risks.
• Assess data readiness for analytics and AI use cases by identifying issues related to bias, data completeness, consistency, and semantic clarity.
• Partner with Data Stewards, Engineers, and Analytics teams to ensure data quality controls are embedded upstream in pipelines supporting AI and advanced analytics.
• Identify, document, and track data quality issues, including root cause analysis and remediation status.
• Provide evidence of data quality control operation to support audits, MR 5f risk reviews, and AI governance assessments.
• Escalate material data quality issues through defined governance channels; does not independently accept data or AI risk.
• Maintain operational metadata, data quality rules, and issue logs for assigned data domains.
• Support enrichment of metadata and lineage to improve data discoverability, explainability, and trust for analytics and AI consumption.
• Ensure data quality findings are traceable to systems, pipelines, and business definitions.

Requirements

• Bachelor’s degree in Data Management, Analytics, Computer Science, Information Systems, or a related field.
• 3–6 years of experience in data quality, data governance, analytics, or data management roles.
• Strong SQL and data analysis skills across large, complex datasets.
• Solid understanding of enterprise data quality concepts and control based operating models.
• Experience with AI assisted or automated data quality tools (e.g., automated profiling, anomaly detection, rule suggestion).
• Understanding of data preparation requirements for analytics and AI use cases.
• Familiarity with metadata management, data lineage, and data catalog practices.
• Ability to collaborate effectively with data engineering, analytics, and governance teams supporting AI initiatives.

Tech Stack

• SQL

Benefits

• Health insurance
• Competitive total compensation package
• Four weeks of vacation
• Four personal days
• Access to training and development opportunities