Associate Director, TQS Data Engineering & Infrastructure

Genmab•Princeton, TX

About The Position

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. Role 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.

Requirements

BS/MS/PhD in Computer Science, Bioinformatics, or a related field
8+ years of data engineering experience (Masters/PhD in a relevant field is a plus)
Proficiency in Python, R, and SQL, with proven experience building scalable, production-grade data pipelines and cloud-based architectures that support translational and clinical research workflows.
Strong experience with Databricks, including Spark (PySpark, SQL), Delta Lake, and Unity Catalog; familiarity with DBT for data transformations within the Databricks ecosystem is a plus.
Hands-on expertise with AWS services such as S3, Glue, Lambda, Step Functions, Data-sync, EMR, Redshift, and core IAM/networking concepts relevant to secure and compliant data engineering.
Experience implementing CI/CD pipelines (GitLab or similar), data testing frameworks, and infrastructure-as-code tooling (e.g., Terraform) to ensure reliable, automated, and scalable data operations.
Familiarity with common translational and clinical data types, such as flow cytometry, cytokine and biomarker assay outputs, genomics/transcriptomics (RNA-seq, DNA-seq), proteomics, and other multi-omics datasets.
Ability to support translational and clinical analysis through basic statistical methods and the development of dashboards or interactive tools (R Shiny, Streamlit, etc.) as well as business intelligence platforms such as Spotfire, Tableau, or Power BI to enable scientific decision-making.
Solid understanding of data governance, security, and compliance requirements in enterprise and research environments, including privacy considerations for clinical and biomarker data.
Experience working in Agile/Scrum environments, with the ability to manage sprint deliverables, collaborate effectively with cross-functional teams, and operate within iterative development cycles.
Knowledge of GxP validation practices and e-system management experience in biotech/pharma R&D environments coupled with a strong understanding of how data flows across research and development stages.

Nice To Haves

Scientific knowledge in oncology is a plus.

Responsibilities

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 partners on data engineering principles, modern data architecture

Benefits

401(k) Plan: 100% match on the first 6% of contributions
Health Benefits: Two medical plan options (including HDHP with HSA), dental, and vision insurance
Voluntary Plans: Critical illness, accident, and hospital indemnity insurance
Time Off: Paid vacation, sick leave, holidays, and 12 weeks of discretionary paid parental leave
Support Resources: Access to child and adult backup care, family support programs, financial wellness tools, and emotional well-being support
Additional Perks: Commuter benefits, tuition reimbursement, and a Lifestyle Spending Account for wellness and personal expenses

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