Bahwan CyberTek
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).
Key Responsibilities
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.
Required Qualifications
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.
Preferred Qualifications
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).
#J-18808-Ljbffr
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).
Key Responsibilities
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.
Required Qualifications
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.
Preferred Qualifications
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).
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