Job Description – Data Quality (DQ) Consultant

Role Purpose

The Data Quality (DQ) Consultant is responsible for designing, implementing, and operationalizing the enterprise Data Quality (DQ) framework to ensure data is accurate, complete, consistent, reliable, and measurable across all business domains and systems. The role works closely with business stakeholders, data governance teams, data engineers, and domain experts to establish data quality standards, monitoring, and continuous improvement processes.

Key Responsibilities

Data Quality Framework

• Design, implement, and maintain the enterprise Data Quality (DQ) framework.
• Define data quality dimensions such as accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity.
• Establish data quality standards, policies, and governance guidelines.

Data Quality Rules & Assessment

• Identify and define Data Quality rules for Key Data Elements (KDEs).
• Perform data profiling and data quality assessments across critical data domains.
• Establish baseline Data Quality scores and define target quality benchmarks.
• Analyze profiling results and recommend improvement initiatives.

Monitoring & Reporting

• Design and implement Data Quality scorecards, dashboards, and KPI reporting.
• Define automated monitoring processes, quality thresholds, alerts, and notifications.
• Develop data quality controls and validation mechanisms across source and target systems.
• Promote continuous monitoring and proactive issue detection.

Issue Management & Remediation

• Lead root cause analysis for Data Quality issues.
• Define and implement Data Quality incident management workflows.
• Establish remediation processes, ownership, escalation procedures, and Service Level Agreements (SLAs).
• Track issue resolution and measure effectiveness of corrective actions.

Data Certification & Audit

• Define and operationalize Data Quality certification processes.
• Establish audit tracking and compliance mechanisms.
• Support governance initiatives by providing quality metrics and certification reports.

Collaboration & Enablement

• Collaborate with Data Governance teams, Domain Consultants, Data Engineers, Data Stewards, and DQ Developers.
• Support implementation and configuration of Data Quality tools and platforms.
• Drive adoption of enterprise Data Quality best practices and continuous improvement initiatives.

Key Deliverables

• Enterprise Data Quality Framework documentation
• KDE-level Data Quality Rules Repository
• Data Profiling Reports and Baseline Quality Scorecards
• Data Quality Dashboards and KPI Definitions
• Data Quality Monitoring and Alerting Framework
• Data Quality Incident Management Process
• Data Quality Certification and Audit Framework
• Root Cause Analysis and Remediation Reports

Required Skills & Experience

• 6–10+ years of experience in Data Quality, Data Governance, or Enterprise Data Management programs.
• Strong understanding of Data Quality dimensions, metrics, and industry best practices.
• Hands-on experience in data profiling, data quality assessments, and quality scorecard development.
• Experience with enterprise Data Quality tools such as Informatica Data Quality (IDQ), Collibra Data Quality, Ataccama, Talend Data Quality, IBM InfoSphere QualityStage, or similar platforms.
• Good understanding of Data Governance, Metadata Management, and Master Data Management (MDM).
• Knowledge of data modeling concepts, ETL processes, and data integration.
• Working knowledge of SQL and data analysis techniques.
• Experience defining data quality KPIs, thresholds, alerts, and monitoring frameworks.
• Strong analytical, problem-solving, and stakeholder management skills.
• Excellent communication, documentation, and presentation abilities.

Preferred Qualifications

• Experience with cloud data platforms such as Azure, AWS, or Google Cloud.
• Knowledge of data catalogs, metadata management, and lineage tools.
• Experience working in Agile delivery environments.
• Certifications in Data Governance, Informatica, Collibra, or related technologies are an advantage.