At Genmab, we're committed to building extra[not]ordinary futures together, by developing antibody products and pioneering, knock-your-socks-off therapies that change the lives of patients and the future of cancer treatment and serious diseases. From our people who are caring, candid, and impact-driven to our business, which is innovative and rooted in science, we believe that being proudly unique, determined to be our best, and authentic is essential to fulfilling our purpose.

The Role

The successful candidate will contribute to the mission of the global data engineering function and be responsible for many aspects of data including creation of data-as-a-product, architecture, access, classification, standards, integration, and pipelines. Although your role will involve a diverse set of data-related responsibilities, your key focus will be on the creation of bioinformatics pipelines to process bulk and single cell genomics and transcriptomics data for the enablement and downstream interpretation of Translational and Quantitative Sciences functions, including Data Science, Translational Medicine, Precision Medicine, and Translational Research. You will have a balance of subject matter expertise in life science data, terminology and processes and technical expertise for hands-on implementation. You will be expected to create workflows to standardize and automate data, connect systems, enable tracking of data, implement triggers and data cataloging. With your experience in the Research domain, you will possess knowledge of diverse assay types such as IHC, flow cytometry, cytokine data, but specialize in genomics and transcriptomics. Your ultimate goal will be to place data at the fingertips of stakeholders and enable science to go faster. You will join an enthusiastic, agile, fast-paced and explorative global data engineering team.

We have a hybrid model that requires being onsite in Princeton, NJ 60% of the time.

Responsibilities
Design, implement and manage ETL data pipelines that process and transform vast amounts of scientific data from public, internal and partner sources into various repositories on a cloud platform (AWS)
Incorporate bioinformatic tools and libraries to the processing pipelines for omics assays such as DNASeq, RNASeq, or proteomics
Enhance end-to-end workflows with automation that rapidly accelerate data flow with pipeline management tools such as Step Functions, Airflow, or Databricks Workflows
Implement and maintain bespoke databases for scientific data (RWE, in-house labs, CRO data) and consumption by analysis applications and AI products
Innovate and advise on the latest technologies and standard methodologies in Data Engineering and Data Management, including recent advancements with GenAI, and latest bioinformatics tools and techniques in RNA sequencing analysis
Manage relationships and project coordination with external parties such as Contract Research Organizations (CRO) and vendor consultants / contractors
Define and contribute to data engineering practices for the group, establishing shareable templates and frameworks, determining best usage of specific cloud services and tools, and working with vendors to provision cutting edge tools and technologies
Collaborate with stakeholders to determine best-suited data enablement methods to optimize the interpretation of the data, including creating presentations and leading tutorials on data usage as appropriate
Apply value-balanced approaches to the development of the data ecosystem and pipeline initiatives
Proactively communicate data ecosystem and pipeline value propositions to partnering collaborators, specifically around data strategy and management practices
Participate in GxP validation processes


Requirements
BS/MS in Computer Science, Bioinformatics, or a related field with 5 years of software engineering experience (8 years for senior role) or a PhD in Computer Science, Bioinformatics or a related field and 2 years of software engineering experience (5 years for senior role)
Excellent skills and deep knowledge of ETL pipeline, automation and workflow managements tools such as Airflow, AWS Glue, Amazon Kinesis, AWS Step Functions, and CI/CD is a must
Excellent skills and deep knowledge in Python, Pythonic design and object-oriented programming is a must, including common Python libraries such as pandas. Experience with R a plus
Excellent understanding of different bioinformatics tools and databases such as STAR, HISAT2, DESeq2, Seurat and how they're used on different types of genomic and transcriptomic data such as single cell transcriptomics
Solid understanding of modern data architectures and their implementation offerings including Databricks' Delta Tables, Athena, Glue, Iceberg, and their applications to Lakehouse and medallion architecture
Experience working with clinical data and understanding of GxP compliance and validation processes
Proficiency with modern software development methodologies such as Agile, source control, project management and issue tracking with JIRA
Proficiency with container strategies using Docker, Fargate, and ECR
Proficiency with AWS cloud computing services such as Lambda functions, ECS, Batch and Elastic Load Balancer and other compute frameworks such as Spark, EMR, and Databricks


For US based candidates, the proposed salary band for this position is as follows:

$114,375.00---$190,625.00

The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. Also, certain positions are eligible for additional forms of compensation, such as bonuses.

About You
You are passionate about our purpose and genuinely...

Equal Opportunity Employer - minorities/females/veterans/individuals with disabilities/sexual orientation/gender identity