Data Modeling Manager

Job title: Data Modelling Manager

• Location: Hyderabad

 About the job

Sanofi is a global life sciences company committed to improving access to healthcare and supporting the people we serve throughout the continuum of care. From prevention to treatment, Sanofi transforms scientific innovation into healthcare solutions, in human vaccines, rare diseases, multiple sclerosis, oncology, immunology, infectious diseases, diabetes and cardiovascular solutions and consumer healthcare. More than 110,000 people in over 100 countries at Sanofi are dedicated to making a difference on patients’ daily life, wherever they live and enabling them to enjoy a healthier life. 

As a company with a global vision of drug development and a highly regarded corporate culture, Sanofi is recognized as one of the best pharmaceutical companies in the world and is pioneering the application of Artificial Intelligence (AI) with strong commitment to develop advanced data standards to increase reusability & interoperability and thus accelerate impact on global health. 

The R&D Data Office serves as a cornerstone to this effort. Our team is responsible for cross-R&D data strategy, governance, and management. We sit in partnership with Business and Digital, and drive data needs across priority and transformative initiatives across R&D. Team members serve as advisors, leaders, and educators to colleagues and data professionals across the R&D value chain. As an integral team member, you will be responsible for defining how R&D's structured, semi-structured and unstructured data will be stored, consumed, integrated / shared and reported by different end users such as scientists, clinicians, and more. You will also be pivotal in the development of sustainable mechanisms for ensuring data are FAIR (findable, accessible, interoperable, and reusable). 

Position Summary:

The R&D Data Modeler develops conceptual and logical data models for initiatives, programs, and cross-R&D capabilities. This role is critical for creation of data models and ontologies, and upkeep of models even beyond the conclusion of a project. Data modelers will apply and assist in the definition and governance of data modelling and design standards, tools, best practices, and related development of any R&D data capability.  

Main responsibilities:

• Engage in data management and analytics projects; understand, advise, and execute on data flow optimization (e.g., data capture, integration and use across R&D) 

• Understand the data-related needs for various cross-R&D capabilities (E.g., data catalog, master data management etc) and associated initiatives  

• Design conceptual and logical data models and data dictionaries/ontologies to cater to R&D business needs and functional requirements; lead validation of physical data models 

• Interact with business, R&D Digital, and other data collaborators to translate needs into data solutions  

• Understand market trends for data modelling tools and metadata management capabilities; provides input into selection of tools and any necessary migration into company’s environment 

• Understand data governance policies, standards and procedures for R&D data 

• Serve as point of contact for data integration topics within solutions (e.g., technology), from source systems to data consumers; define process, tools, and testing requirements 

• Maintain modelling and naming standards, and data harmonization, metadata management, and source-to-target data mapping documentation 

• Evaluate and influence projects while serving as “voice-of-business”; map systems/interfaces for data management, set standards for future state, and close gap from current-to-future state 

• Serve as technical and data consultant for R&D initiatives with major platforms/technology implementations 

• Maintain documentation and act as an expert on data definitions, data standards, data flows, legacy data structures / hierarchies, common data models, data harmonization etc. for R&D functions 

• Educate and guide R&D teams on standards and information management principles, methodologies, best practices, etc. 

Deliverables

• Conduct requirements gathering from business analysts, data scientists, and other stakeholders 

• Formulate strategies, and query optimizations to enhance data retrieval speed 

• Develop complex and scalable data models aligned to organization’s long term strategic goals 

• Formulate data governance frameworks, policies, and standards 

• Establish best practices for data modeling ensuring interoperability among systems, applications & data sources 

About you 

• Experience in Data Modelling,  business data management, information architecture, technology or other related field  

• Demonstrated ability to understand end-to-end data use and business needs  

• Knowledge of R&D data and data domains (e.g., across research, clinical, regulatory etc) 

• Experience with creating and applying data modelling best practices and naming conventions 

• Strong analytical problem-solving skills 

• Demonstrated strong attention to detail, quality, time management and customer focus 

• Excellent written and oral communications skills 

• Strong networking, influencing and negotiating skills and superior problem-solving skills 

• Demonstrated willingness to make decisions and to take responsibility for such 

• Excellent interpersonal skills (team player) 

• Experience with data management practices and technologies (e.g., Collibra, Informatica etc) 

• Familiar with databases (relational, dimensional, NoSQL, etc) and concepts of data integrity 

• Strong knowledge of data architecture (e.g., data lake, data virtualization, hubs etc) and modelling (e.g., 3nf etc) is required, Experience in big data infrastructures (E.g., Hadoop, NOSQL etc) 

• Experience with SDLC and pharma R&D platforms; experience with requirements gathering, system design, and validation/quality/compliance requirements, Experience managing technology and/or data warehouse projects  

• Familiarity with relationship databases and entity-relationship data modelling, Experience with hierarchical data models from conceptualization to database optimization 

• Education: Bachelor’s in computer science, Engineering, Mathematics, Statistics, or related; Masters preferred