Data Steward

Overview 

Join the Alnylam Commercial Data and Analytics team and help build a product-driven engine that turns data-driven insights into deployed decisions with measurable business impact. As part of Customer Experience and Innovation, you will work cross-functionally with technical and business partners to accelerate commercial outcomes by standardizing what we build, how we build it, and how we deliver it through the tools and workflows business users rely on every day.

As Associate Director, Data Steward, you will contribute to prioritized Value Teams across the commercial organization: Business Performance & Drivers, Customer & Account Growth, Omnichannel & Media Optimization, Patient Pull-Through & Outcomes, and Evaluation & Learning. Value Teams collaborate fluidly to deliver and manage decision products that enable specific business outcomes, driving competitive foresight and actioning for the commercial organization through predictive and prescriptive analytics.

In this role, you will bring product thinking to commercial data foundations by designing the gold-level models and governed metrics that make decision products reusable, trusted, and scalable. You will enforce single source of truth standards while enabling fast delivery through clear documentation, testing, and predictable data products. You will work within and across multiple Value Teams, balancing standards and stewardship with practical enablement so teams can move faster without breaking trust.

The position will be based in Cambridge, MA.

Key Responsibilities

• Work within and across multiple Value Teams, aligning to transparent priorities and delivering governed data products that scale across franchises and markets.
• Own gold-level data modeling and architecture for key commercial domains, including conformed entities and reusable dimensional models that support analytics and platforms.
• Establish and maintain single source of truth metrics, including canonical definitions, lineage, documentation, and version history with disciplined change control.
• Own analytics engineering standards for the gold layer, including dbt model architecture, modular design, testing, documentation, and performance considerations.
• Operate data quality management, including automated tests, monitoring, incident triage, root-cause analysis, and remediation coordination with engineering partners.
• Define and deliver reliable data products with SLAs for freshness and availability that support decision products and self-serve insight surfaces.
• Partner with Advanced Analytics to ensure metrics reflect decision intent and are fit for measurement and learning loops, and partner with AI and ML Engineering to ensure feature inputs are stable and traceable through approved versioned pathways.