Data Steward

Profile: A data steward aims at guaranteeing that data is professionally managed at all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse), by supporting researchers in a specific domain to keep the quality, integrity, and access arrangements of data and metadata in compliance with the applicable law, institutional and/or funder policies, and individual permissions.

Competence areas/Skills: RDM services (repositories, data management planning, etc.), knowledge management and metadata, data sharing and publishing, data standards, code development, community management, soft skills (communication, organization, independence).

Tasks:

• informing and advising other stakeholders (scientists, IT engineers, management, etc.) on data management best practices in their respective domain in accordance with international policies and guidelines (including raising awareness, training, ensuring compliance with the applicable law);
• designing and implementing institutional data management workflows and practices ensuring quality and interoperability of (meta)data (including support to data management planning and identification of requirements for adequate data infrastructures and tools);
• assisting in modelling and publishing data in accordance with (inter)national principles for findability, accessibility, interoperability, and reusability (including data curation);
• facilitating community building to achieve convergence on best practices for FAIR data management (including networking, participation to international networks and initiatives, dissemination).

System Engineer

Profile: A system engineer is in charge of designing, implementing and maintaining the information technology systems for an organization, designing the storage infrastructure, establishing networking rules for cybersecurity and ensuring systems compliance with established quality and safety standards.

Competence areas/Skills: operating systems such as Linux, UNIX and Windows, containerized deployments, scripting languages, basic Layer 2 and 3 networking, virtualization platforms, system performance monitoring, security, IT automation, cloud platforms, data management, modern authentication protocols for web authentication and federation.

Tasks:

• maintaining and integrating various systems, including development and maintenance of scripts and automation for storage system;
• hosting, organization, configuration and migration of data management systems and association of metadata to the relevant data set via automated systems;
• management of authentication and authorization (Identity and Access Management) on systems and among systems;
• monitor and test system operations and performance;
• ensure systems compliance with established quality and safety standards;
• working with researchers to understand the dataset to be archived (data life cycle from live to frozen data, peculiarities or intrinsic characteristics of data depending on the specific domain, size of the data collected, timing for saving and backup of data, etc.).

Application Engineer

Profile: An application engineer is responsible for applying and maintaining advanced tools and technologies for the management of scientific data coming from different scientific fields, providing where possible common solutions.

Competence areas/Skills: data modelling, data processing and analysis, cloud computing, API integration, Git versioning system and practices, installing, configuring and troubleshooting application software and system management tools, scripting languages, privacy-by-design and privacy-by-default principles, SQL and no-SQL db and tools, container and devops technologies.

Tasks:

• development, maintenance and monitoring of software infrastructure and tools for managing research data throughout the research lifecycle in compliance with institutional policies;
• provide assistance to the integration of the data infrastructure with institutional informative systems and computational facilities;
• providing support to staff and researchers on using software and tools;
• producing technical and end-user documentation of systems and tools;
• contribute to building from scratch software and service components by exploiting external services or by developing custom solutions (e.g., software applications based on disciplinary standards for data collection, organization, flow, integration, preservation and sharing).

Software Developer

Profile: A software developer in this context is responsible, in collaboration with the team, for producing code, technical frameworks, user interfaces, and systems for modelling, analysis, storage, presentation of research data, so that they are available for Machine Learning and AI.

Competence areas/Skills: software engineering practices and best practices for the full software development life cycle with special reference to back-end technologies, data processing technologies and front-end frameworks and technologies. API integration, containerization and devops technologies, Git versioning system and practices, cloud computing, data modelling, Machine Learning (ML) frameworks like TensorFlow or PyTorch.

Tasks:

• design, development and maintenance of software solutions to answer requests coming from RDM project and integration with the institutional data infrastructure;
• developing tools for data acquisition, transfer and integration;
• producing code, technical frameworks, user interfaces, and systems for modelling, analysis, storage, presentation of research data;
• defining requirements of technical solutions and overseeing the design and development of software;
• advising on the technical feasibility of projects within a technically complex environment.