Job Title     : Data Modeler  
Experience : 8-12 Years
Location     : Remote      

Job Description : 

• The R&D Data Modeler designs, implements, and governs the data architecture / data design standards that
• underpins our R&D analytics and digital initiatives. Partnering closely with business, digital, and IT teams, this
• role will translate complex R&D data needs into robust conceptual, logical, and physical data models ensuring
• consistency, scalability, interoperability and reusability across our data ecosystem.

Essential Functions of the job:

• Data Modeling & Architecture
• Design and maintain conceptual, logical, and physical data models aligned to R&D business
• needs and strategic objectives.
• Develop and govern common data models and ontologies for Research, Clinical, Regulatory, and other R&D domains.
• Collaborate with data product teams to design, prototype, and deliver reusable, scalable data
• products translating core models into actionable datasets.
• Validate physical schemas for performance and scalability, minimizing data latency for
• consumers (including self-service analytics and AI model training).

• Solution Design & Integration

• Serve as the SME for data integration, mapping source-to-target flows from legacy systems
• through the Operational Data Repository to consumer applications.
• Define and maintain modeling standards, naming conventions, and harmonization processes
• to guarantee data consistency across structured and unstructured data platforms.
• Partner with R&D business, DSDI, GTS and other teams to translate requirements into end-to-
• end data solutions, including metadata management and lineage documentation.

• Governance & Best Practices

• Embed data governance frameworks, policies, standards, and procedures, into the modeling
• lifecycle, ensuring FAIR data principles and compliance with GxP regulations.o Establish best practices for model versioning, documentation, and change management in
• alignment with SDLC and validation processes.
• Evaluate emerging data modeling tools and technologies (e.g., cloud-native modeling,
• metadata repositories) and advise on selection and migration strategies.

• Consulting & Enablement

• Act as the “data translator” in cross-functional working groups, facilitating workshops to
• uncover data requirements, perform impact analyses, and drive consensus on future-state
• architectures.
• Provide expert guidance and training to R&D teams on data modeling methodologies,
• metadata management, and information management principles.
• Support major platform implementations (e.g., data warehouse, data lake, virtualization hubs)
• as the principal data consultant.

Supervisory Responsibilities:

• No

Computer Skills:

• Microsoft Applications (Outlook, Word, Excel, PowerPoint, Project)

Qualifications:

• Experience in business data management, information architecture, or other related field
• Bachelor’s degree in Computer Science, Information Systems, Data Management, or related field.
• Hands-on experience in data modeling, data or information architecture within Pharma R&D.
• Demonstrated ability to design scalable, complex data models from conceptualization through physical
• optimization, aligned to business needs.
• Strong understanding of R&D data domains (Research, Clinical Operations, Safety, Regulatory) and
• related compliance requirements (GxP, validation).
• Solid grasp of data integration patterns, ETL/ELT pipelines, and source-to-target mapping.
• Experience with data governance and data management tools (Databricks, Informatica, Reltio, ERwin,
• and storage platforms for structured and unstructured data like Azure, AWS, Oracle both cloud and on- prem)
• Familiarity with SDLC, change control, and quality/compliance processes.
• Excellent analytical, communication, and stakeholder-management skills.
• Strategic thinker with an eye for detail and a passion for data excellence.
• Collaborative mindset, adept at influencing across matrixed organizations.
• Proactive learner who stays current on data modeling trends and best practices.
• Customer-focused, with a commitment to delivering high-quality, fit-for-purpose data solutions.

Travel:

• Minimal