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Data Engineering Manager
Novartis
Hyderabad, IND
Full-time
Senior · 8+ years experience
Onsite
Discovered 3 weeks ago
Master Data Management (MDM)ReltioSnowflakeSQLJIRASalesforce Lifesciences Cloud
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Master Data Management (MDM)ReltioSnowflake
Key Skills for This Role
Master Data Management (MDM)ReltioSnowflakeSQLJIRASalesforce Lifesciences Cloud
Full Job Posting
Business Analysis & Requirements Management
- Lead business requirement workshops, stakeholder interviews, and discovery sessions across global and local markets.
- Elicit, analyze, document, and validate business, functional, and non-functional requirements.
- Act as the primary liaison between Global Product teams, Country MDM teams, business stakeholders, and IT teams.
- Conduct impact assessments to identify business, process, architectural, and technical dependencies affecting MDM initiatives.
- Translate business needs into BRDs, FRDs, user stories, epics, use cases, process flows, and acceptance criteria.
- Define UAT strategies, business test scenarios, and acceptance criteria.
- Clarify and refine requirements throughout the project lifecycle and obtain business sign-off.
- Facilitate requirement handover sessions to delivery, development, and support teams.
Master Data , Data Model and Data Quality
Define and document requirements related to Customer Master Data, Brand Master and Reference Data and Hierarchies.
Analyse master data entities, relationships, affiliations, business rules, and data models.
Support development and enhancement of global data models, business glossaries, and master data standards.
Evaluate local market needs against global standards and recommend scalable, harmonized solutions.
Drive data standardization, hierarchy management, and attribute harmonization initiatives
Define/Validate data quality rules, validation criteria, monitoring processes, and KPI frameworks.
Monitor data quality metrics including completeness, accuracy, consistency, uniqueness, and timeliness.
Partner with business, technical and operations teams to continuously improve data quality and adoption.
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