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Analyst - Data Quality

NOVARTIS
Hyderabad50K-2 LPA Posted 14 Apr 2025
FULL TIME
kpi reports
Data Lineage

Job Description

Job description

Data Governance team is setup to develop and maintain the right metadata, master data, data quality parameters, data lineage, KPI reports and workflows to ensure that data is governed, consistent, reliable and easily accessible for decision-making while ensuring data is maintained across TechOps as per Pharmaceutical regulatory and compliance requirement.

Analyst Data Quality focus on improving Master Data Quality and enabling Master Data Governance by publishing KPIs & Reports. Drives Data Quality enhancement across the organization for consistency, correctness and completeness. Support and facilitate data enabled decision making for Novartis internal customers by providing quality data.

About the Role

Major accountabilities:

  • Analyze data quality enrichment and cleansing requests from Data Stewards and functional SMEs
  • Envision the Data Quality strategy, metrics and framework
  • Implement data cleansing and linking strategy as per documented data quality improvement strategy
  • Execute plan for Data Quality corrections for various functions and light house projects
  • Liaise with the Data Stewards, SMEs and Data Solution Designer to execute continuous and active Data Governance
  • DQ KPI measurements, Gap analysis & Continuous improvement to ensure that active Data Governance is achieved, and data quality meets business need and complies with mandatory standards
  • Develop data quality rules and provide analytical support to business by publishing Excel / QlikView / QlikSense / PowerBi reports for Data quality
  • Perform root cause analysis on data inconsistencies highlighted by users and recommending improvements. Capture business requirements for data management functionality and translating the information to developers to help building effective information flow
  • Develop and confirm data dictionary rules ensuring business understands the rules and its value for improving data quality
  • Ensure adherence to the established Data quality processes, which includes 100% compliant to training completion for self and associates, proactive CAPA update and preparation for audit readiness.

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