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Data Engineering Manager
NOVARTIS
foundit
Hyderabad / Secunderabad, Telangana, India
7-13 years
1L-7L
Full time
30 April 2026
Top Skills:
Pharma / Life ScienceSales & MarketingCommercial OperationsAgileCComplianceContinuous ImprovementData ManagementData ModelingData QualityData WranglingEnterpriseGmpGovernanceHealthcareLinked DataProcurementR&dRegulatory RequirementSale & MarketingService DeliverySupply Chain

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Job Description iconJob Description
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Key Responsibilities:

  • Bring deep domain understanding in Commercial operations, Sales & Marketing. Experience in working in Pharma / Life Science industry is strongly preferred
  • Collaborate with business partners, data scientists and IT teams to create and implement world class data products and solutions
  • Can act as a hands-on engineer in curating data, onboarding data assets to the FAIRification process and managing data quality and standards
  • Brings good understanding of enterprise approved schema, tools and platforms and can use them to deliver high quality clean and linked data across projects
  • Manage quality and consistency of NBS CONEXTS data services delivery to all partners
  • Ensure compliance with GMP and regulatory requirements and continuous improvement of quality relevant processes within area of responsibility
  • Deliver on data strategy priorities and DLC activities in an AGILE manner with appropriate documentation and communication throughout the delivery of services.

Essential Requirements:

  • Masters/ PhD in Computer Sciences / IT or other quantitative sciences
  • 7+ years experience in a Global company as a data steward, engineer, modeler or data scientist
  • Business understanding of pharmaceutical industry and data standards. Domain experience in at least one of the following areas - a) Pharma R&D, b) Manufacturing, Procurement and Supply Chain and c) Marketing and Sales. Experience in working in Pharma / Life Science industry and US healthcare data is strongly preferred.
  • Understanding of data modeling (conceptual, logical, and physical) using different data modeling methodologies, understanding of semantic modeling techniques and graph databases
  • Experience on data wrangling including Ingestion, Unification, Anonymization, Search, Master & Meta Data Management and Governance.