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.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLdata analysisdata quality controlsdata profilingdata validationdata reconciliationautomated profilinganomaly detectionroot cause analysisdata governance

Soft Skills

collaborationcommunicationproblem-solvinganalytical thinkingattention to detail