Job
Description
As a Senior Data Engineer at Novartis, you will play a vital role in leading the technical foundation of AI-powered commercial solutions. Your responsibilities will include designing, developing, and deploying scalable data pipelines, managing technical delivery from vendor partners, and acting as a liaison between business stakeholders and engineering teams to translate business needs into actionable data solutions. Key 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 and engineering teams, translating business needs into actionable data solutions. - Integrate and harmonize complex data from multiple sources, both internal systems and third-party providers. - Apply data governance, lineage tracking, and data quality monitoring aligned with compliance needs. - Recommend innovative data engineering solutions and best practices to enhance data infrastructure performance and efficiency. - Collaborate with AI/ML engineers to ensure data readiness for model training, scoring, and operationalization. - Guide and mentor junior data engineers to contribute to the growth of the commercial data engineering capability within Novartis. Qualifications Required: - Bachelor's or master's degree in computer science, engineering, or a related field. - 7+ years of experience in data engineering, with 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, and Azure Data Factory. - Demonstrated ability to manage vendor teams and ensure quality delivery across geographically distributed teams. - Strong communication and stakeholder management skills, with 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, and syndicated datasets. - Familiarity with privacy and compliance frameworks relevant to healthcare data, such as HIPAA, GDPR, and GxP. - Experience working in Agile, cross-functional product teams. About Novartis: Novartis is committed to building an outstanding, inclusive work environment and diverse teams that are representative of the patients and communities they serve. If you are passionate about making a difference in patients' lives and thrive in a collaborative environment, Novartis is the place for you. [Note: Additional details about the company were not present in the provided job description.] As a Senior Data Engineer at Novartis, you will play a vital role in leading the technical foundation of AI-powered commercial solutions. Your responsibilities will include designing, developing, and deploying scalable data pipelines, managing technical delivery from vendor partners, and acting as a liaison between business stakeholders and engineering teams to translate business needs into actionable data solutions. Key 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 and engineering teams, translating business needs into actionable data solutions. - Integrate and harmonize complex data from multiple sources, both internal systems and third-party providers. - Apply data governance, lineage tracking, and data quality monitoring aligned with compliance needs. - Recommend innovative data engineering solutions and best practices to enhance data infrastructure performance and efficiency. - Collaborate with AI/ML engineers to ensure data readiness for model training, scoring, and operationalization. - Guide and mentor junior data engineers to contribute to the growth of the commercial data engineering capability within Novartis. Qualifications Required: - Bachelor's or master's degree in computer science, engineering, or a related field. - 7+ years of experience in data engineering, with 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, and Azure Data Factory. - Demonstrated ability to manage vendor teams and ensure quality delivery across geographically distributed teams. - Strong communication and stakeholder management skills, with 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, and syndicated datasets. - Familiarity with priva
