Senior Data Product Engineer, Commercial

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 requires 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 operates as a hands‑on technical lead, defining frameworks, making architectural decisions, and guiding implementation patterns across dbt, Snowflake, Databricks and BI platforms. Familiarity with commercial data sets such as IQVIA One Key, Veeva Open Data, Veeva CRM, Integrichain, Symphony Non‑Retail, Claims data, Digital, Omnichannel and Precision lab data is required. 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, Python/S3, 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 HIPAA 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 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, LifeScience Cloud) Market research and syndicated data (IQVIA, etc.) Support data pipelines used for incentive compensation, field force effectiveness, and brand performance analytics. Platform Engineering & Automation Automate deployment pipelines with CI/CD (GitHub Actions, GitLab CI, or AWS CodePipeline) for dbt and Databricks. Implement infrastructure‑as‑code (IaC) for reproducibility (Terraform, CloudFormation). Ensure system reliability through observability and monitoring (Datadog, CloudWatch, Prometheus, or similar). Benchmark and optimize SQL, Spark, and BI query performance at scale. Collaboration & Stakeholder Support Partner with Data Scientists, Commercial Analysts, and Business Partners to translate business needs into technical solutions. Work closely with IT, Compliance, and Data Privacy teams to ensure GDPR‑aligned data handling. Provide technical guidance on data availability, feasibility, and best practices. Required Qualifications 5+ years in data engineering, analytics engineering, or data platform development. 10+ years of experience IT with some in Biotech/Pharma industry. Expert‑Level Proficiency DBT: advanced macros, Jinja, testing, exposures, dbt Cloud deployment. Databricks: Spark (PySpark, SQL), Delta Lake, Unity Catalog. Snowflake AWS: S3, Glue, Lambda, Step Functions, Datasync, EMR, Redshift, IAM and networking/security fundamentals. Data Visualization: Power BI or Tableau. Strong programming background in Python and SQL (including query optimization). Proven experience with distributed systems and large‑scale datasets (TB scale). Experience implementing CI/CD pipelines, data testing, and infrastructure as code. Solid understanding of data governance, security, and compliance in enterprise environments. Why This Role? Working as a Data Engineer in an oncology company means your work directly supports the people who need it most—patients facing one of the most difficult diagnoses of their lives. Every pipeline you build, every dataset you harmonize, and every insight you enable helps accelerate how quickly life‑changing therapies reach the right patients at the right time. You get to solve challenging engineering problems while contributing to something bigger than technology. You're not just optimizing pipelines—you're helping ensure that a patient gets diagnosed earlier, a doctor receives better information, or a family gains access to a therapy that gives them more time together. You will work at the intersection of modern data engineering and analytics product development, solving challenges of scale, performance, and usability. This role is ideal for someone who thrives in a hands‑on engineering environment and wants to build the technical foundation for data‑driven decision‑making across the organization. Compensation For US based candidates, the proposed salary band for this position is as follows: $131,600.00—$197,400.00. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. Certain positions are eligible for additional forms of compensation, such as discretionary bonuses and long‑term incentives. 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. About You You are genuinely passionate about our purpose. You bring precision and excellence to all that you do. You believe in our rooted‑in‑science approach to problem‑solving. You are a generous collaborator who can work in teams with a broad spectrum of backgrounds. You take pride in enabling the best work of others on the team. You can grapple with the unknown and be innovative. You have experience working in a fast‑growing, dynamic company (or a strong desire to). You work hard and are not afraid to have a little fun while you do so! Locations Genmab maximizes the efficiency of an agile working environment, when possible, for the betterment of employee work‑life balance. Our offices are crafted as open, community‑based spaces that work to connect employees while being immersed in our powerful laboratories. Whether you're in one of our office spaces or working remotely, we thrive on connecting with each other to innovate. #J-18808-Ljbffr