Associate Director, TQS Data Engineering & Infrastructure

At Genmab, we are dedicated to building extra[not]ordinary® futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines® that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals’ unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees.

Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so.

Does this inspire you and feel like a fit? Then we would love to have you join us!

Support the mission of the Translational & Quantitative Science (TQS) data engineering function by creating high-quality data products that integrate preclinical and clinical translational datasets.

The TQS department at Genmab brings together capabilities across bioanalytical and biomarker sciences, translational biology, clinical and quantitative pharmacology, and in vivo sciences. We deliver strong scientific rationale to drive pivotal go/no-go decisions and inform and accelerate (clinical) development of our highly differentiated antibody therapies. We generate and work with a diverse data ecosystem, including non-clinical and clinical biomarker data, multi-omics datasets, imaging, flow cytometry, pharmacokinetic/pharmacodynamic data, real-world data sources, and more.

This role blends hands‑on development with technical leadership, guiding the full lifecycle of data products to ensure they align with business goals and operate on scalable, reliable data infrastructure. The candidate will partner closely with scientific teams to understand bespoke research needs and architect fit‑for‑purpose data/analytics workflows and infrastructure that meet the unique demands of translational research, while ensuring that solutions align with modern data technology stacks and contemporary engineering best practices.

Success requires expertise in data product management, data/analytics engineering, scientific knowledge of translational and clinical data, and the ability to design strategic, technically sound solutions that advance TQS within the R&D data ecosystem.

• Design, develop, and maintain scalable data products and pipelines that integrate preclinical, clinical, and translational datasets, ensuring reliability, performance, and reproducibility.
• Lead the end‑to‑end lifecycle of TQS data products, including discovery, prototyping, architecture definition, implementation, validation, and deployment in partnership with enterprise data engineering team.
• Architect modern data solutions using modern engineering patterns (e.g., lakehouse principles, modular pipelines, metadata‑driven design) and develop fit‑for‑purpose data models, ETL/ELT workflows, and analytical infrastructure that meet the diverse needs of translational research.
• Apply data product management principles to define features, requirements, and success metrics, ensuring data products deliver measurable scientific and operational value, while also guiding and managing the performance of the enterprise engineering team responsible.
• Ensure data quality and governance controls are embedded throughout pipelines, including validation, lineage capture, and adherence to safety, privacy, and regulatory expectations (i.e., HIPAA, GDPR, etc.).
• Partner with enterprise engineering teams to deliver scalable, automated, and maintainable infrastructure and deployment workflows, and drive data engineering excellence by enforcing best practices in code quality, CI/CD pipelines, testing, observability, and documentation within TQS’s data engineering organization.
• Prototype new data or analytics approaches to evaluate emerging technologies, tools, or frameworks that could enhance TQS data capabilities.
• Mentor team members and scientific…