Job Description Summary
At Novartis, we are reimagining medicine by using data and digital technologies to transform how we engage with patients, healthcare professionals, and healthcare systems. As a Senior Data Engineer within the Data & Digital Commercial organization, you will lead the technical foundation of AI-powered commercial solutions—helping drive more effective engagement, access, and performance globally and within local markets.
This mid-management role blends hands-on data engineering with delivery oversight, vendor management, and stakeholder collaboration. You'll help create the pipelines and data platforms that enable AI/ML applications in sales effectiveness, omnichannel marketing, customer segmentation, and real-world commercial insights
Job Description
Roles and Responsibilities:
- Lead the design, development, and deployment of scalable, production-grade data pipelines for commercial AI products.
- Manage technical delivery from vendor partners, ensuring high-quality output aligned with Novartis standards, timelines, and budgets.
- Serve as a bridge between business stakeholders (global and country-level commercial teams) and engineering, translating business needs into actionable data solutions.
- Integrate and harmonize complex data from multiple sources, including internal systems (Veeva, CRM, sales ops) and third-party providers.
- Apply and promote data governance, lineage tracking, and data quality monitoring aligned to compliance needs
- Recommend innovative data engineering solutions and best practices that enhance the performance and efficiency of the data infrastructure
- Collaborate with AI/ML engineers to ensure data readiness for model training, scoring, and operationalization.
- Guide and mentor junior data engineers while contributing to the growth of the commercial data engineering capability within Novartis.
Essential Requirements:
- Bachelor’s or master’s degree in computer science, engineering, or related field.
- 7+ years of experience in data engineering, including at least 2+ years in a leadership or delivery oversight role.
- Strong hands-on experience with Python, SQL, Spark/PySpark, and tools like Databricks, Airflow, Snowflake, Azure Data Factory.
- Demonstrated ability to manage vendor teams and ensure quality delivery across geographically distributed teams.
- Strong communication and stakeholder management skills; experience working with commercial business leads in pharma or life sciences.
- Basic understanding of pharmaceutical commercial data at both country and global levels, including CRM data, sales performance data, field force activity, and syndicated datasets.
- Familiarity with privacy and compliance frameworks relevant to healthcare data (HIPAA, GDPR, GxP).
- Experience working in Agile, cross-functional product teams.
Preferred Qualifications:
- Direct experience in pharma commercial domains such as market access, promotional analytics, or omnichannel customer engagement.
- Knowledge of HCP/HCO identity resolution, customer 360 strategies, or AI-based targeting and segmentation use cases.
- Exposure to MLOps workflows and how data pipelines support AI product deployment at scale.
Commitment to Diversity and Inclusion:
Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.
Accessibility and accommodation
Novartis is committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to perform the essential functions of a position, please send an e-mail to [email protected] and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
Skills Desired
Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling
