Director, Enterprise Data Management & Governance Lead

NewAmsterdam Pharma Corporation

21d•Remote

About The Position

NewAmsterdam is an innovative biotech company with the experience and expertise to deliver across its ambitious clinical, regulatory, and commercial goals. Our robust scientific approach focuses on advancing a new era of life-saving treatments for lipid-related diseases. Our vision is that people at risk for heart disease can take control of their health and live longer, fuller lives. Our team is led by a world-class team of industry veterans and key opinion leaders, including some of the world’s preeminent cardiometabolic experts and we foster a passionate, supportive culture that is backed by a high level of integrity and inclusivity. This role exists to make our data provable on demand and fast to get, so that proving it never requires a fire drill. You will own enterprise data governance across NewAmsterdam, master data, and the data layer of the cloud data platform that Data, AI & Innovation operates on Google Cloud. All three, deliberately: standards that live only in policy documents do not survive an inspection or a launch, and a platform without standards produces numbers nobody will defend. Data, AI & Innovation owns and operates the data platform; IT owns the source systems, and the business functions own the data within them. This role sets the standards those systems are expected to meet and governs the data flowing from them. You will be measured as much on how fast people get trustworthy data as on how well it is controlled.

Requirements

8-10 years in data governance, data management, or data platform leadership, including demonstrated experience leading teams.
Life sciences experience, including familiarity with GxP data integrity — Part 11, Annex 11, ALCOA+ — and how governance controls interact with system validation.
An enterprise data governance program you designed and stood up, not inherited.
Hands-on master data ownership, plus current depth in a modern cloud data platform — including hands-on experience with Google Cloud Platform (GCP) — and working fluency in SQL and [dbt or equivalent].
Experience delivering governance from outside IT, and getting business owners to accept accountability for data without authority over them.

Nice To Haves

Designing GxP readiness into a platform ahead of validation, or maintaining a validated system in a cloud and agile context
A first commercial launch or clinical-to-commercial transition
CDISC standards and CRO data flows
Commercial pharma data (customer master, distributor and specialty pharmacy, claims, patient services)
GDPR at scale and privacy operations at the data layer
SOX ITGC
CDMP or equivalent

Responsibilities

Enterprise data governance strategy, policy, and the federated stewardship model, sequenced against regulatory and launch milestones. You will co-chair the Data Governance Council with Quality Assurance.
Metadata, business glossary, and end-to-end lineage across our clinical, commercial, and corporate data.
The data quality program, with domain scorecards reported to the executive team.
Master data end to end across clinical, commercial, and regulatory domains.
GxP readiness for the data layer. The platform is not in validated scope today. As our data increasingly supports regulated decisions, that will change — and this role decides where a validated zone belongs, defines the criteria that trigger it, and designs the boundary before we need it rather than retrofitting it under inspection pressure.
Data management practice and the platform's data layer. Modeling standards, SLAs for certified data products, and governance of the CRO and vendor data supply chain. You will be hands-on in architecture reviews, access model design, and data quality investigations.
The commercial data foundation ahead of US launch, including the controls behind transparency reporting and the commercial–medical firewall.
The partner data interface — net sales, channel, and safety data exchange — where it feeds revenue recognition, supply planning, and pharmacovigilance.
Data governance for AI: provenance, controls on what enters models, and EU AI Act readiness.
Work as a peer to Security, Legal, Privacy, Compliance, and QA, translating their obligations into implementable data controls without absorbing their decisions.