£92 Per hour
Inside
Remote
England, United Kingdom
Summary: The Project Manager for Advanced Data Domains will oversee technical projects focused on developing an event-driven data pipeline for scientific data. This role involves leading a cross-functional team and ensuring the successful integration of the BioMed Graph pipeline with various systems. The position requires expertise in managing complex data engineering projects, particularly within biomedical domains. The contract is for six months with the possibility of extension, starting in September 2025.
Key Responsibilities:
- Own project delivery across all phases—from planning and execution to risk mitigation and reporting—for the BioMed Graph platform.
- Translate complex architectural requirements into a clear, deliverable roadmap.
- Partner with domain experts and ontology owners to align technical delivery.
- Collaborate with technical leads to achieve incremental delivery.
- Coordinate with quality teams on data integrity and assurance.
- Manage integration points across tools e.g. Jira, Confluence.
Key Skills:
- Expert experience working as a technical project manager delivering complex data engineering or scientific computing projects.
- Strong familiarity with event-driven systems, streaming data platforms, and stateful transformations.
- Experience working in biomedical or scientific data domains is highly preferred.
- Comfort navigating ambiguity and evolving requirements, particularly in a research-aligned agile environment.
- Proven success working across cross-functional squads and communicating technical concepts to non-technical stakeholders.
- Strong planning, prioritization, and risk mitigation skills in a hybrid Agile environment.
- Excellent communication, documentation, and stakeholder engagement abilities.
Salary (Rate): £80.00/hr
City: England
Country: United Kingdom
Working Arrangements: remote
IR35 Status: inside IR35
Seniority Level: Mid-Level
Industry: IT
Job Title: Project Manager - Advanced Data Domains
Location: UK/Remote
Start Date: September 2025
Day Rate: Up to £690 a day
Inside IR35
Job Type: 6-month contract (with scope to extend)
Company Introduction
We are seeking a contract Project Manager with experience managing technical projects in advanced data domains. You will lead a cross-functional team delivering an incremental event-driven data pipeline for the organisation, enabling continuous enrichment, retraction, and versioning of scientific data. You’ll work with internal data engineers, ontologists, architects, data scientists, and taxonomy experts. Your mission is to ensure that the BioMed Graph pipeline is delivered and integrated with consuming systems, achieving business and scientific goals.
Required Skills/Experience
- Expert experience working as a technical project management experience delivering complex data engineering or scientific computing projects.
- Strong familiarity with event-driven systems, streaming data platforms, and stateful transformations.
- Experience working in biomedical or scientific data domains is highly preferred
- Comfort navigating ambiguity and evolving requirements, particularly in a research-aligned agile environment.
- Proven success working across cross-functional squads and communicating technical concepts to non-technical stakeholders.
- Strong planning, prioritization, and risk mitigation skills in a hybrid Agile environment.
- Excellent communication, documentation, and stakeholder engagement abilities.
Job Responsibilities/Objectives
- Own project delivery across all phases—from planning and execution to risk mitigation and reporting—for the BioMed Graph platform.
- Translate complex architectural requirements into a clear, deliverable roadmap.
- Partner with domain experts and ontology owners to align technical delivery
- Collaborate with technical leads to achieve incremental delivery.
- Coordinate with quality teams on data integrity and assurance.
- Manage integration points across tools e.g. Jira, Confluence
Nice to Have
- Experience integrating machine learning outputs (e.g., relation predictions) into production knowledge graphs.