Senior Data Product Engineer, Commercial

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!

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.

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.

• 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.

• 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.

• 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 and harmonization of EU commercial datasets such as:
• HCP/HCO master data (IQVIA and Veeva)
• Sales and distribution data (Integrichain, Symphony Non‑Retail and Country specific market data)
• Omnichannel engagement (email, rep‑triggered, web, events)
• CRM activity data (Veeva, Life Science Cloud)
• Market research and syndicated data (IQVIA, etc.)
• Support data pipelines used for incentive compensation, field force effectiveness, and brand performance analytics.

• Automate deployment pipelines with CI/CD (Git Hub Actions, Git Lab CI, or AWS Code Pipeline) for dbt and Databricks.
• Implement infrastructure‑as‑code (IaaC) for reproducibility (Terraform, Cloud Formation).
• Ensure system reliability through observability and monitoring (Datadog, Cloud Watch, Prometheus, or similar).
• Benchmark and optimize SQL, Spark, and BI query performance at scale.

• Partner with Data Scientists, Commercial…