Clinical Data Engineering Lead

Role Summary

Lead Clinical Data Engineering within the Oncology Data Science team at Novartis Biomedical Research. This onsite role is based in Cambridge, MA. You will shape early clinical development by building biomarker data infrastructure, champion translational research, unlock AI-powered discoveries, and raise the bar for operational excellence in biomarker data and multimodal analytics across Novartis’ oncology trials. You will lead and develop a high-performing clinical data engineering team and collaborate across functions to advance oncology programs.

Responsibilities

• Define and implement the clinical data engineering roadmap in alignment with Novartis’ data and digital strategy, collaborating with SMEs and OncDS leadership.
• Integrate advanced tools and AI/ML-ready infrastructure to support predictive modeling, multimodal analytics, and real-world data applications.
• Align clinical and pre-clinical data engineering initiatives with the broader oncology strategy.
• Lead, manage, and develop a high-performing clinical data engineering team, fostering collaboration and growth.
• Drive strategic initiatives and partnerships across a matrixed organization.
• Oversee data ingestion, transformation, and validation processes for clinical trial data, ensuring compliance with GCP/GxP, CDISC, and SOPs.
• Collaborate with CROs and internal teams to optimize data flow, versioning, and retention policies.
• Build and optimize data pipelines for both structured and unstructured clinical data to enable advanced analytics and informed decision-making.
• Deploy scalable solutions for data harmonization, metadata management, and interoperability across platforms such as Foundry, Domino, Snowflake, and POSIT Connect.
• Develop and manage applications and visualization tools, contributing to novel data products that support clinical decision-making and enable AI-driven initiatives in oncology trials.

Qualifications

• Required: This position will be located at the Cambridge, MA site and remote work is not available; travel 0-3% as defined by the business.
• Required: Master's degree in computer science, Bioinformatics, Data Engineering, Software Engineering or closely related discipline; PhD preferred.
• Required: Minimum 10 years of hands-on experience architecting and managing clinical data engineering, data management, and bioinformatics solutions in the pharmaceutical or biotechnology industry.
• Required: Demonstrated expertise in designing, implementing, and scaling data infrastructure to support clinical development—including AI/ML-driven analytics and multimodal data integration.
• Required: Proven ability to define, document, and operationalize end-to-end assay data generation and processing pipelines, with a focus on automation, orchestration, and compliance.
• Required: Extensive experience with oncology clinical trials, including regulatory-compliant management of clinical biomarker data and application of data standards (CDISC, SDTM, ADaM).
• Required: Deep familiarity with FAIR (Findable, Accessible, Interoperable, Reusable) data principles, data harmonization, and enterprise data governance frameworks.
• Required: Strong leadership in technical teams, with advanced communication and stakeholder management skills.
• Preferred: Extensive experience leading cross-functional data science initiatives in oncology, including translational science, biomarker analysis, real-world data, and exploratory clinical research; proven expertise with NGS technologies, and modern bioinformatics tools.
• Preferred: Advanced proficiency in cloud-native architectures, data lakes, and visualization frameworks (RShiny, Dash, Spotfire); strong programming and engineering skills (R, Python, Java, shell scripting, Linux, HPC), with a deep understanding of GxP, Agile methodologies, AI/ML operations, and architecting/managing AI agents in large clinical data environments.