Data Quality Senior Analyst

Job Description

Roles Responsibilities : Data Quality, Modelling Root Cause Analysis Utilize deep expertise in SAP MDG/ECCs and Azure Data Lake (via Databricks) using SQL/Python to perform root cause analysis on assigned data quality use cases. Develop and refine both conceptual and physical data models to automate monitoring of ongoing data quality issues. Facilitate multiple workshops to explore and select the most effective modelling options. Collaborate with business stakeholders (Data Owners and Data Stewards) to profile data, identify quality issue patterns, and assess impacts on critical data elements (CDEs) relevant to each business area. Quantify financial impacts of data quality issues and articulate both quantitative and qualitative business benefits of remediation efforts, managing implementation timelines effectively. Schedule and lead regular working groups with business units to drive progress on root cause analysis (RCA), remediation actions, or to prepare issues for discussion in Data Governance Forums (DGFs). Define business data quality rules that form the basis for KPIs and measures, feeding into dashboards and workflows for continuous monitoring and timely issue escalation. Maintain comprehensive understanding of the data quality value chain, including CDE concepts, issue management, KPIs, and governance. Lead execution of data quality assessments to support process improvements and BAU initiatives. Proactively identify hidden or emerging data quality risks and escalate to Lead/Manager as necessary. Communicate clearly and effectively, articulating complex data quality issues and their business implications to diverse, outward-facing audiences. Dashboarding Workflow Management Support the design, development, and deployment of data quality dashboards using Power BI to provide clear visibility into data health and trends. Establish escalation protocols and construct workflows with alerts that notify process and data owners about unresolved data quality issues, ensuring timely resolution. Collaborate with IT and analytics teams to incorporate advanced technologies such as AI, Machine Learning, and cognitive science to innovate data quality processes. Data Quality Improvement Plans Partner with business functions and projects to create and embed actionable data quality improvement plans, fostering accountability and business ownership. Set clear targets for data quality maturity and monitor progress, intervening proactively when improvements lag expectations. Support ongoing initiatives focused on cleaning and enhancing the existing data landscape. Project Delivery Governance Support Lead and direct Data Quality Analysts in executing profiling, establishing conversion criteria, and resolving complex data quality issues from both technical and business perspectives. Own and produce data quality deliverables aligned with DAS data change methodology and ensure data quality is integrated into Gold and Silver level data change projects. Contribute insights to the development of business cases by quantifying the cost of poor data and the benefits of remediation.