IUCN, in collaboration with the University of Melbourne and several Red List of Ecosystems partners, is rebuilding the Red List of Ecosystems (RLE) data platform. The new system will support the storage, management, and dissemination of RLE data, enable the timely delivery of new assessments, and improve the use of results for decision-making.

The development phase will focus on:
• Streamlining workflows for data ingestion, storage, validation, and publication.
• Enhancing the user experience to improve access to existing assessments and accelerate the production of new ones.
• Integrating AI and machine-learning methods, where appropriate, to support analysis and assessment processes.
• Ensuring interoperability with related systems, including other IUCN data products such as the Red List of Threatened Species and relevant external systems.

The Data & Platform Manager – Red List of Ecosystems, will play a central role in shaping the overall vision for the Red List of Ecosystems (RLE) data platform. This new system will support the storage, management, and dissemination of RLE data, enable the timely delivery of new assessments, and improve the use of results for decision-making. The position will be key in coordinating with partners and stakeholders and driving the delivery of the IUCN RLE platform.
The purpose of this position is to lead product strategy, planning, and execution for the Red List of Ecosystems digital platform, encompassing both web development and data management. The role ensures that the platform meets user needs, aligns with IUCN’s strategic priorities, and adheres to responsible AI practices and robust data governance standards.
The Data & Platform Manager – Red List of Ecosystems will also work closely with the IUCN Conservation Informatics team (including the Red List Data Manager and the Red List Systems Manager), the IUCN Program Manager for the Red List of Ecosystems, and a range of external collaborators from partner institutions to achieve these goals.

The Data & Platform Manager – Red List of Ecosystems will be responsible for the development, maintenance, and long-term sustainability of the Red List of Ecosystems (RLE) database and associated digital platforms. The role combines IT product management and data management functions, working closely with scientific teams, IT partners, and external data providers.

1.Product Strategy & Roadmapping
• Develop, maintain and update a long-term product roadmap for the Red List of Ecosystem’s digital platform, data infrastructure, and AI-assisted tools, ensuring sustainability beyond immediate project cycles.
• Define and articulate product vision, success metrics, and phased delivery plans for platform development, maintenance, and evolution.
• Facilitate workshops with stakeholders and partners to align priorities.
• Prioritise features and technical enhancements using measurable criteria (impact, feasibility, risk, scalability, data maturity, AI readiness).
• Ensure alignment with IUCN’s digital transformation policies, the Conservation Data Hub vision, and interoperability between Red List of Ecosystems and other IUCN data products like the Red List of Threatened Species
• Contribute to planning for long-term platform resourcing, technical debt management, and lifecycle management of software and data assets.

2. Requirements Gathering & User Experience
• Manage external consultant work/service provision, on user research, system design or technical development ensuring to translate outputs into formal requirements and user stories.
• Map end-to-end data flows: ingestion, validation, versioning, assessment, review and publication.
• Ensure user-centred design principles are applied, with strong attention to accessibility, usability, multilingual support, and varying technical capacities of global users.
• Maintain up-to-date system and data flow documentation to support onboarding, knowledge transfer, and long-term maintainability.

3 AI & Data Functionality
• Identify, assess and prioritize use cases where AI/ML can support RLE processes
(e.g., automated data checks, geospatial anomaly detection, ecological similarity modelling, NLP support for documentation).
• Collaborate with scientists and developers to design safe and transparent AI features.
• Define evaluation metrics and quality assurance processes for AI supported functions.
• Support the design and development of data pipelines, , metadata standards, and data models
• Contribute to development of embedding, Model Context Protocols (MCP), Retrieval Augmented Generation (RAG) and any other AI standards and techniques where appropriate.

4 Project Coordination & Delivery
• Coordinate with partners the product backlog, sprint planning, and release schedules.
• Coordinate activities across software developers, DevOps, data engineers, and scientific contributors.
• Oversee integration with existing IUCN systems.
• Monitor delivery progress, dependencies, and risks, and proactively address delays or resource constraints.

5 Data Management, Governance, Risks & Compliance
• Act as data steward for the RLE database, ensuring high standards of data quality, consistency, completeness, and documentation.
• Establish and maintain data governance processes, including data standards, validation rules, version control, audit trails, and long-term archiving strategies.
• Ensure appropriate data sharing agreements, licences, and permissions are in place with partners and contributors, in line with IUCN policies and international best practice.
• Define a data governance framework with the definition of accountability and standard processes for monitoring data quality.
• Establish data security policies with different degrees of protection according to different categories and with role definition that defines access
• Champion data integrity creating procedures to protect the Red List of Ecosystems data from data loss and inaccuracies.
• Set up a data and disaster recovery plan.
• Ensure compliance with IUCN data policies, responsible AI guidelines, and privacy/ethics principles.
• Identify and mitigate risks related to data quality, cybersecurity, AI bias, and system reliability.
• Maintain documentation of data models, APIs, standards, and governance decisions.

6 Stakeholder Engagement
• Coordinate consultations, feedback sessions, and user-testing cycles to continuously improve platform functionality and data workflows.
• Communicate product decisions and rationales clearly to both technical and non-technical audiences.
• Support capacity building by contributing to guidance materials, training resources, and best-practice documentation for data contributors and users.