SAP MDG Technical Analyst with Azure/ (Data lake and Data Bricks)

Job Description

SAP MDG/SAP ECC experience (T codes, Tables structures etc) Azure Data lake /AWS/Data Bricks Leveraging SAP MDG/ECCs experience the candidate is able to deep dive to do root cause analysis for assigned use cases. Also able to work with Azure data lake (via data Bricks) using SQL/Python. Data Model (Conceptual and Physical) will be needed to be identified and built that provides automated mechanism to monitor on going DQ issues. Multiple workshops may also be needed to work through various options and identifying the one that is most efficient and effective Works with business (Data Owners/Data Stewards) to profile data for exposing patterns indicating data quality issues. Also is able to identify impact to specific CDEs deemed important for each individual business. Identifies financial impacts of Data Quality Issue. Also can identify business benefit (quantitative/qualitative) from a remediation standpoint along with managing implementation timelines. Schedules regular working groups with business that have identified DQ issues and ensures progression for RCA/Remediation or for presenting in DGFs Identifies business DQ rules basis which KPIs/Measures are stood up that feed into the dashboarding/workflows for BAU monitoring. Red flags are raised and investigated Understanding of Data Quality value chain, starting with Critical Data Element concepts, Data Quality Issues, Data Quality KPIs/Measures is needed. Also has experience owing and executing Data Quality Issue assessments to aid improvements to operational process and BAU initiatives Highlights risk/hidden DQ issues to Lead/Manager for further guidance/escalation Support designing, building and deployment of data quality dashboards via PowerBI Determines escalation paths and constructs workflow and alerts which notify process and data owners of unresolved data quality issues Collaborates with IT & analytics teams to drive innovation (AI, ML, cognitive science etc.) Works with business functions and projects to create data quality improvement plans Sets targets for data improvements / maturity. Monitors and intervenes when sufficient progress is not being made Supports initiatives which are driving data clean-up of existing data landscape Owns and develops relevant data quality work products as part of the DAS data change methodology Ensures data quality aspects are delivered as part of Gold and Silver data related change projects Supports the creation of business cases with insight into the cost of poor data