Principal Research Data Architect

Job summary

This role is essential for establishing a data-driven future in drug discovery. You will lead the development and management of a research data foundation, ensuring it is a governed and semantically-consistent source for analysis and machine learning workflows. You will collaborate with research and IT teams to enhance legacy systems and cloud data platforms, gradually taking full ownership of the data ecosystem.

Qualifications

• Extensive experience in designing research or scientific data architectures, preferably in drug discovery or life sciences.
• Hands-on experience with scientific data platforms (e.g., Benchling) and requirements engineering (URS/FRS).
• Strong knowledge of semantic technologies, including ontologies and knowledge graphs.
• Familiarity with cloud lakehouse platforms (e.g., Databricks) and data pipelines for analytics and AI/ML.
• Proven track record in data governance, including metadata and lineage management.
• Experience leading teams and driving architecture direction across stakeholders.

Responsibilities

• Own and develop the research data model, capturing scientific requirements and directing new workflow configurations.
• Co-lead the migration of legacy assay data into the new data foundation.
• Build a semantic layer by adapting public ontologies and controlled vocabularies.
• Manage research data governance, ensuring data quality and lineage.
• Architect the research data lake and tactical knowledge graphs for analytics and AI.
• Collaborate with AI/ML and computational chemistry teams to ensure data flows effectively into analysis layers.

Skills

• Leadership and influence in cross-functional settings.
• Pragmatic approach to system design and delivery.
• Ability to work effectively in a mid-migration ecosystem.

Education

• Relevant degree in a scientific or technical field is preferred.

Tools

• Experience with scientific data platforms, cloud lakehouse platforms, and semantic technologies.