Senior Data Quality Analyst

Essential Experience and Job Requirements:

• • 11 total years of experience predominantly in Oil & Gas or Financial Services/Banking industry within Data Management space

  • Experience of working with Data Models/Structures and comfortable in deepdiving to design and fine tune them

  • Experience of Data Quality Management i.e. Governance data quality management (root cause analysis Remediation /solution identification) Governance Forums (papers production quorum maintenance Minutes publication) CDE identification Data Lineage (identification of authoritative data sources) preferred. Understanding of KPIs/Measures needed as well

  • Experience of having worked with senior stakeholders in multiple Data Domain/Business Areas CDO and Technology. Ability to operate in global teams within multiple time zones

  • Ability to operate in a dynamic and changing setup and be able to identify priorities. Comfortable to operate independently without too much direction.

Desirable criteria

• • SAP MDG/SAP ECC/CFIN experience (T codes Tables structures etc.)

  • Azure Data lake /AWS/Data Bricks

  • Creating dashboards & workflows (powerBI Qlikview or Tableau etc.)

  • Creating analytics and insight in a DQ setting (powerBI/ powerQuery)

  • Profiling and analysis skills ( Informatica or Collibra)

  • Persuading influencing and communication at a senior level management level

  • Certification in Data Management Data Science Python/R desirable

KEY ACCOUNTABILITIES

• • Data quality framework: Demonstrate deep understanding of data quality value chain encompassing Critical Data Element concepts data quality issue management DQ KPIs/Measures and DQ remediation. Perform Data Quality Issue assessments to aid improvements in operational process and BAU initiatives

  • Data Profiling: Work with business (Data Owners/Data Stewards) to profile data to uncover patterns indicating data quality issues and converts them into rules for ongoing monitoring

  • Business analysis and data quality rules definition: Elicit business requirement through discussions with data owners business SMEs program team to document business DQ rules supporting KPIs/Measures for BAU monitoring.

  • Design thinking and modelling: Leverage knowledge of SAP data structures to perform data and impact analysis for assigned usecases by accessing Azure data lake (via dataBricks) using SQL/Python. Define model (conceptual) and functional design to support automated DQ monitoring. Chair workshops with data governance analysts developers and data integration team to work through various options and come up with optimal design. Participate in testing of solutions and ensure they are fit for purpose for business use.

  • Visualization and dashboarding: Support design build and deployment of high quality actionable reports and dashboards (leveraging Power BI) which detect poor data quality and help business drive resolution. Determines escalation paths and constructs workflow and alerts which notify process and data owners of unresolved data exceptions requiring actions. Collaborates with IT & analytics teams to drive innovation by leveraging AI ML NLP etc.

  • Root cause analysis and impact assessment: Come up with models to calculate financial impacts of Data Quality Issue. Further identify business benefit (quantitative/qualitative) from a remediation standpoint while managing implementation timelines. Participate in data governance forums to present data quality finding and recommend plan of action for resolution.

  • Data quality issue management and remediation: Work alongside the data governance and data remediation team to uncover DQ issue based on exceptions and come up with approach while parallelly driving business accountability and ownership

  • Project Management: Own and drive data quality projects against the plan and ensure risks/issues are identified and escalated well in time.

  • Collaboration and Communication: Build a rapport with business stakeholders technology team program team and wider digital solution and transformation team to suggest areas to make a difference through the implementation of data governance framework.

Interested can contactOR email resume to

""data quality","Data quality framework","large data set","Azure","MDG","SAP","power apps","enormal tool","Data cleansing","data governance","Data Profiling","Design thinking and modelling',"data modelling","Visualization and dashboarding","Project Management","SAP ECC","CFIN","Tcodes","Data Bricks","powerBI","powerQuery","DQ setting"