Senior Business Analyst

Senior Business Analyst - R&D Data Strategy (ELN Focus)

Overview

We are seeking an exceptionally experienced and highly skilled Senior Business Analyst to join our R&D Information Technology team of a Biotech company. This role is critical to a strategic, high‑visibility project focused on implementing a “Best‑of‑Breed” Electronic Lab Notebook (ELN) solution across our global research organization. The successful candidate will have a minimum of 10 years of progressive experience and a deep, hands‑on understanding of data management challenges within a modern research lab environment.

This role will be instrumental in solving complex data harmonization and global dashboard reporting issues arising from the simultaneous use of multiple ELN platforms (e.g.,
Benchling
, and other similar tools).

• Lead the analysis and documentation of the native data models for all current and proposed ELN solutions, specifically focusing on platforms like Benchling
  .
• Design and facilitate the creation of a Semantic Data Model that acts as the unifying layer for data originating from disparate ELNs, ensuring data integrity and consistency for downstream use.
• Define and document the data mapping, transformation, and governance rules required to bridge the gap between source ELN data models and the target Semantic Data Model.
• Template and Standardization Development:
  • Work directly with global research scientists and lab heads across various disciplines (e.g., Chemistry, Biology, Materials Science) to gather requirements for standard ELN templates.
  • Design, test, and implement global, standardized ELN templates (e.g., experimental procedures, assay registration, material tracking) to drive consistency in data capture and metadata annotation.
  • Establish best practices and training materials for template use and compliant data entry.
• Global Reporting and Dashboarding Enablement:
  • Translate high‑level business needs for Global Dashboard Reporting into detailed, actionable functional and non‑functional requirements
    .
  • Ensure the Semantic Data Model supports efficient querying and aggregation necessary for enterprise‑level reporting and business intelligence (BI) initiatives.
  • Collaborate with Data Engineering and BI teams to validate data pipelines and reporting solutions.
• Stakeholder Engagement and Change Management:
  • Serve as the primary liaison between R&D scientists, IT developers, and project leadership.
  • Facilitate workshops and design sessions to achieve consensus on data standards and business processes.
  • Contribute to change management activities, ensuring successful adoption of new data standards and ELN templates.

• Minimum 10+ years of experience as a Business Analyst, Data Analyst, or Data Architect in the Life Sciences, Pharmaceutical, or Biotechnology R&D domain
  .
• Non‑Negotiable:
  Prior, direct working experience within a research lab (academic or industry) and a deep understanding of the R&D lifecycle (from target identification to process development).
• Deep Technical Expertise: Proven, hands‑on experience with the data models, architecture, and configuration of at least one major commercial ELN/LIMS/LES platform (e.g.,
  Benchling
  , Dotmatics, or similar).
• Data Modeling Acumen: Expert‑level knowledge of conceptual, logical, and physical data modeling techniques, and demonstrable experience in developing Semantic Data Models or Enterprise Data Dictionaries.
• Communication: Exceptional verbal and written communication skills with the ability to articulate complex data concepts to both technical and non‑technical audiences (i.e., scientists and executives).
• Soft Skills: Proven ability to drive consensus, manage demanding global stakeholders, and lead requirement gathering in a highly regulated and fast‑paced environment.

• Experience working with data integration tools and technologies (ETL/ELT).
• Familiarity with master data management (MDM) principles as applied to R&D entities (e.g., Samples, Batches, Assays).
• Experience with reporting and visualization tools (e.g., Tableau, Power BI) and understanding of how data structure impacts report performance.
• Knowledge of industry data standards and frameworks (e.g., FAIR principles, Allotrope, Pistoia Alliance).