Key Responsibilities:

• Solution Design: Architect and design data solutions using tools like AWS, Snowflake, and Databricks.
• Project Management: Oversee the delivery of data lake projects, including data acquisition, quality, transformation, and publishing.
• Collaboration: Work closely with business stakeholders to understand requirements and deliver solutions that meet their needs.
• Innovation: Stay updated with industry trends and emerging technologies to drive continuous improvement.
• Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.
• Leveraging Technology for business and customer needs.
• Solid understanding of Analytical and technical frameworks for descriptive and prescriptive analytics.
• Awareness of integration patterns across MDM/RDM and transactional systems.
• Production experience delivering data lake projects, including data acquisition, quality, transformation, and publishing.
• Strong exposure to data security and governance policy definitions and enforcement capabilities.
• Data product-centric approach to defining solutions. Collaborate with business in gathering requirements, grooming product backlogs, driving delivery, and ongoing data product enhancements.
• Agile delivery experience managing multiple concurrent delivery cycles.
• Sound foundation in Analytical Data life cycle management.
• Awareness of Data product change Management and risk mitigation.

Key Performance Indicators:

• Achieved targets in Enterprise business case contribution, KPIs, customer satisfaction, and innovation measures 
• Business capability, vision & strategy clearly defined, communicated, and executed, well aligned to business strategy and Enterprise IT strategy, and providing a competitive advantage to Novartis
• Role model with the highest standards of professional conduct in leading the business capability area in line with the new IT operating model
• Deployment of digital platforms and services at scale to deliver the digital strategy
• Operational Efficiency
  • Data Processing Time: Average time taken to process and make data available for analysis.
  • System Uptime: Percentage of time the data systems are operational and available.
• Stakeholder Satisfaction:
  • Stakeholder Feedback Score: Ratings from internal stakeholders on the usefulness and accessibility of data.
  • Issue Resolution Time: Average time taken to resolve data-related issues reported by stakeholders.
• Innovation and Improvement:
  • Implementation of New Technologies: Number of new data tools or technologies successfully integrated.
  • Process Improvement Initiatives: Number of initiatives aimed at improving data architecture processes.
• Project Delivery:
  • On-time Delivery Rate: Percentage of projects completed on or before the deadline.
  • Project Success Rate: Percentage of projects that meet their objectives and deliver expected outcomes.
• Data Utilization:
  • Data Usage Rate: Frequency and extent to which data is accessed and used by the commercial team.
  • Insights Generated: Number of actionable insights derived from data analytics.

Skills:

• Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.
• Solid understanding of Analytical and technical frameworks for descriptive and prescriptive analytics.
• Strong familiarity with AWS, Databricks, and Snowflake service offerings.
• Experience integrating disparate analytical and visualization platforms.
• Strong knowledge of MLOps and project life cycle management.
• Strong exposure to data security and governance policy definitions and enforcement capabilities.
• Data product-centric approach to defining solutions. Collaborate with business in gathering requirements, grooming product backlogs, driving delivery, and ongoing data product enhancements
• Agile delivery experience managing multiple concurrent delivery cycles.
• Sound foundation in Analytical Data life cycle management.
• Awareness of Data product change Management and risk mitigation.
• Strong analytical and problem-solving skills, effective communication, and the ability to influence and collaborate with cross-functional teams

Education: 

• Bachelor’s degree in computer science, engineering, or a related field.

Desirable:

Experience with pharmaceutical data and familiarity with global data sources