Data Quality Manager

Roles & Responsibilities :

Data Quality, Modelling & Design Thinking

• Lead, mentor, and inspire a team of data analysts and engineers, fostering a collaborative, innovative, and high-performance culture.
• Oversee the design, implementation, and continuous improvement of data quality models using SAP MDG, Azure, and Power Platform technologies. Guide the team to develop appropriate conceptual data models foundational to physical data model creation.
• Engage closely with technology and business stakeholders to gather requirements, identify improvement areas, and clearly articulate the business value of data quality use cases including cleansing, validation, and enrichment.
• Demonstrate deep understanding of the Data Quality value chain—from Critical Data Elements (CDEs) to Data Quality KPIs—and how it integrates with overarching Data Governance frameworks. Lead and manage within this context effectively.
• Facilitate workshops and training sessions to promote data quality awareness, best practices, and a culture of continuous improvement across the organization.
• Conduct performance evaluations, provide coaching, and identify opportunities for the professional development of team members.
• Proactively identify and escalate data quality risks or hidden issues to the Data Quality Manager for resolution.

Dashboarding & Workflow Management

• Conceptualize, design, and deploy robust data quality dashboards using Power BI to monitor and visualize data health effectively.
• Develop escalation paths and workflows with alerts that notify process and data owners of unresolved data quality issues, ensuring timely remediation.
• Collaborate with IT and analytics teams to incorporate cutting-edge technologies such as AI, Machine Learning, and cognitive science to enhance data quality monitoring and automation.

Data Quality Improvement Plans

• Partner with business functions and projects to develop and embed sustainable data quality improvement plans, fostering business ownership and accountability.
• Define and track measurable targets for data quality maturity, actively monitoring progress and intervening when improvements fall short of expectations.
• Support data clean-up initiatives that improve the accuracy and reliability of existing data landscapes.

Project Delivery & Oversight

• Lead and coordinate Data Quality Analysts in executing data profiling, conversion criteria establishment, and resolution of complex technical and business data quality issues.
• Own the detailed project delivery plan for multiple concurrent data quality programs, ensuring clarity on roles, responsibilities, and timelines using effective RACI frameworks.
• Drive cross-functional alignment and successful delivery of data quality objectives within large-scale enterprise programs.

Qualifications

Educational qualification:

B.E

Experience :

• 11 to 15 years of comprehensive experience in Data Management, preferably within the Oil & Gas or Financial Services/Banking industries.
• Proven expertise in working with complex data models and structures, with strong capability to deeply analyze, design, and optimize these models for enhanced data quality and usability.
• Extensive experience in Data Quality Management, including governance practices, Data Quality Issue (DQI) management—covering root cause analysis, remediation, and solution identification. Familiarity with facilitating governance forums including preparation of papers, ensuring quorum, and publishing meeting minutes.
• In-depth knowledge of Critical Data Element (CDE) identification and Data Lineage, including identifying authoritative data sources. Strong understanding of relevant KPIs and data quality measurement frameworks.
• Experience of having worked with senior stakeholders across multiple data domains, business units, Chief Data Office (CDO), and technology teams. Comfortable operating within global, multi-time zone environments.
• Ability to work effectively in dynamic, fast-changing environments, with strong prioritization skills and the ability to work independently with minimal supervision.