Data Quality Control Management

Saaki Argus And Averil Consulting; Chennai District; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Saaki Argus And Averil Consulting is seeking a Data Quality Manager to develop, implement, and maintain data quality standards and controls across Risk, Finance, Compliance, and Regulatory Reporting within the APAC region. You will operationalize data quality management using Collibra DQ and Collibra DIP, ensure BCBS 239 compliance, collaborate with Data Owners and Risk/Finance teams, and design end-to-end DQ rules, thresholds, dashboards, and remediation workflows for regulatory reporting.

Qualifications

• Experience leading data quality programs and governance
• Strong knowledge of regulatory reporting requirements
• Ability to translate data quality into measurable controls and dashboards

Responsibilities

• Develop and maintain DQ standards, rules, and controls
• Collaborate with Data Owners, Risk, Finance, and Compliance teams
• Operationalize DQ using Collibra tools and integrate with data platforms
• Provide training and awareness on data quality policies and tools

Skills

Data governance

Data quality management

Regulatory reporting

Stakeholder collaboration

Risk management

Tools

Collibra DQ

Collibra DIP

Azure Data Lake

Alteryx

Vermeg

Job description

Role SummaryAs a Data Quality (DQ) Manager, you will be responsible for developing, implementing, and maintaining data quality standards, rules, and controls, primarily across Risk, Finance, Compliance, and Regulatory Reporting functions within the APAC region. The role focuses on operationalizing data quality management using Collibra Data Intelligence Platform (DIP) and Collibra DQ, while ensuring compliance with banking regulatory frameworks such as BCBS 239.You will work closely with Data Owners, Data Stewards, Risk Officers, Finance teams, Compliance functions, and Regulatory Reporting teams to identify and resolve data quality issues and establish effective DQ controls for regulatory and business-critical reporting.Key ResponsibilitiesImplement and maintain data governance and data quality policies, standards, and procedures, ensuring alignment with regulatory requirements such as BCBS 239 and applicable global and regional policies.Partner with Risk, Finance, Compliance, and Regulatory Reporting teams to identify, investigate, and remediate data quality issues and gaps.Conduct training and awareness sessions on data quality policies, standards, processes, and supporting tools. Engage stakeholders to define, review, agree upon, and maintain data quality standards.Design, implement, and maintain Data Quality (DQ) rules, controls, and thresholds for Risk, Finance, Compliance, and Regulatory Reporting functions across APAC.Develop functional DQ controls covering areas such as:Risk metrics and risk reportingGeneral Ledger controlsFinancial reconciliationsCompliance dataRegulatory reporting requirementsCritical data elements and key reporting attributesCollaborate with business and technology teams to establish DQ rules for critical regulatory reports, risk metrics, and business use cases.Ensure appropriate data quality, traceability, and lineage for risk aggregation and risk reporting in accordance with BCBS 239 principles.Work with teams responsible for Regulatory Reporting platforms, solutions, and workflows to embed DQ controls into Collibra DQ and other existing data quality tools.Support the integration of Collibra with platforms such as Azure Data Lake, Alteryx, and Vermeg to collect, monitor, and present data quality results.Develop and maintain data quality dashboards and reporting mechanisms to monitor DQ scores, regulatory compliance, data quality exceptions, and remediation progress.Configure and administer Collibra DQ rules, workflows, scorecards, and monitoring processes.Support the integration of Collibra DQ with the Collibra Data Intelligence Platform (DIP), Business Glossary, and Data Dictionary to establish consistent data definitions, ownership, lineage, and quality controls.Drive continuous improvement of the organization's data quality framework, ensuring that critical data is accurate, complete, consistent, timely, valid, and fit for regulatory and business purposes.