Overview

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! Role Overview We are looking for a Senior Data Product Engineer with deep expertise in DBT, Databricks, Snowflake, Power BI/Tableau and AWS, who can architect and implement scalable, reliable, and high-performance data products. This role will require strong technical skills across data modeling, distributed data processing, ELT pipeline development, and dashboarding, with an emphasis on automation, observability, and engineering best practices supporting our US, EU and UK commercial data warehouse. You will be expected to design and deliver production-grade data pipelines and models, optimize query, and compute performance, and expose data to end users through well-structured semantic layers and dashboards. The ideal candidate is conceptually strong in modern data architectures and principles, capable of adapting across tools rather than being limited by them and operate as a hands-on technical lead, defining frameworks, making architectural decisions, and guiding implementation patterns across dbt, Snowflake, Databricks and BI platforms. The ideal candidate should have familiarity with commercial data sets like IQVIA One Key, Veeva Open Data, Veeva CRM, Integrichain, Symphony Non-Retail, Claims data, Digital, Omnichannel and Precision lab data. Work arrangement: This role offers flexibility to work away from the office for 20%-40% of a typical schedule. Employees may use this work schedule in increments of single days or multiple consecutive days, provided it does not exceed 40% within a 60-day period, and is approved by the hiring manager. Core Responsibilities Data Engineering & Architecture Architect and implement end-to-end ELT workflows with DBT (core and Cloud), ensuring modular, testable, and reusable transformations. Build high-performance data pipelines in Snowflake and Databricks (PySpark, Delta Lake, Unity Catalog) for batch and streaming workloads. Orchestration using Apache Airflow and Amazon tools. Engineer scalable data ingestion pipelines into AWS (DBT, SQL, PythonS3, Kinesis, Glue, Lambda, Step Functions) with strong monitoring and fault tolerance. Ensure observability, cost efficiency & scalability in all pipeline and compute designs. Data Modeling, Governance, Security and Compliance Design normalized and star-schema models for analytical workloads, following dbt's best practices and software engineering principles. Implement data quality testing frameworks (dbt tests, Great Expectations, or custom validations) with automated CI/CD integration. Manage data versioning, lineage, and governance through tools such as Unity Catalog and AWS Lake Formation. Ensure data lineage, cataloging, and metadata management are maintained. Apply HIPPA privacy rules, and pharma specific compliance standards. Support audits, documentation, and validation activities. Analytics Enablement & Dashboards Develop semantic data layers that support self-service analytics across BI tools (Tableau, Power BI etc.). Partner with analysts and data scientists to optimize queries and deliver production-ready datasets. Manage ingestion