Director, Enterprise Architecture

Alnylam Pharmaceuticals•Cambridge, MA

2d•$215,900 - $292,100•Hybrid

About The Position

Alnylam is the world’s leading RNAi therapeutics company. Our 2030 ambition depends on world-class data, engineering, and AI capabilities that help accelerate science, strengthen operations, and scale innovation across the enterprise. The Director, Enterprise Architecture will define the reference architectures, technical standards, and governance patterns that enable data, AI, and integration solutions to be built consistently across a regulated life sciences environment. This role reports to the Head of Data, AI & Engineering and is expected to directly lead one to two architects while influencing a broader matrixed technology organization spanning ReDev, Development, Manufacturing & Supply, Commercial, and G&A. This role will partner closely with the Senior Director of AI & Analytics, who owns model strategy and delivery, and the Senior Director of Platform Engineering & Data Products, who owns the platform build. This is a hybrid role based in Cambridge, MA, with two days per week onsite.

Requirements

10 or more years of experience in architecture, engineering, or related technology leadership roles, including a meaningful portion in biotech, pharmaceutical, life sciences, or another highly regulated environment.
Demonstrated ability to architect and ship production systems at enterprise scale, with continued hands-on fluency in code, platform patterns, and technical design tradeoffs.
Working knowledge of GxP validated systems and the practical application of 21 CFR Part 11, EU Annex 11, and GAMP 5 to data platforms, integration patterns, and AI-enabled systems.
Experience with regulated data domains such as clinical, regulatory, safety and pharmacovigilance, quality, manufacturing and supply, commercial, HCP, HCO, or patient-level data.
Knowledge of privacy, transparency, data governance, and AI governance expectations, including lineage, cataloging, classification, stewardship, data quality controls, model risk classification, and third-party AI assessment.
Experience defining reference architectures, technical standards, reusable patterns, and architecture governance processes for enterprise-scale data, AI, integration, or platform environments

Nice To Haves

Experience with modern data, AI, and platform technologies such as Snowflake, dbt, Fivetran, Astronomer, Monte Carlo, AWS, Collibra, Microsoft 365, Copilot, or comparable tools and platforms.
Experience with AI governance, responsible AI controls, agent and tool authorization patterns, model risk classification, or vendor AI assessment.
Experience building platform, cloud, or software engineering capabilities in financial services, complex manufacturing, large-scale technology platforms, or other regulated environments.
Prior people leadership experience, including leading architects or senior technical contributors.
Minimal travel expected.

Responsibilities

Define enterprise reference architectures and technical standards for data, AI, integration, semantic layers, data products, and the platform capabilities required to support governed AI adoption.
Own the Gate 0 architecture review process, including vendor AI assessments, architecture decision records, reusable pattern libraries, and standards that reduce duplication across teams.
Prototype and pressure-test architecture decisions in partnership with engineering teams, translating working builds into reusable patterns before they are adopted as enterprise direction.
Design the technical enforcement layer for enterprise data governance, including lineage, catalog and glossary integration, classification, data quality controls, stewardship workflows, policy-driven access, and master/reference data patterns.
Establish scalable architecture patterns across clinical, regulatory, safety, quality, manufacturing, commercial, and G&A domains so regulated data can be governed consistently without bespoke solutions for each area.
Define integration, retrieval, authorization, identity propagation, and audit patterns for AI-enabled systems, including agent and tool use and governed model access to enterprise data.
Partner with AI, analytics, platform engineering, network, security, and business technology leaders to ensure architecture decisions are coherent, secure, practical, and aligned to regulated life sciences requirements.