Coordinate across teams to ensure consistent data product development and utilisation, establishing and delivering the defined data architecture. This includes working with the Data Architect to understand team needs, use cases, and constraints for the use case data ecosystems.
Work with both data producers and consumers to optimise existing data products and the data within them to meet evolving business needs. Advocate for teams delivering data-as-a-product.
Collaborate on the design of the research data ecosystem, addressing disambiguation, data product creation, API development, model building, harmonisation, standardisation, and governance.
Adopt company-standardized technology, including cloud platforms, and collaborate with technology teams to improve offerings. Consider the best technology for data teams, given a spectrum of technical literacy.
Work with data privacy and governance teams to ensure data security and appropriate accessibility, adhering to relevant regulations (e.g., GDPR).
Foster a safe and collaborative technical community, growing technical knowledge and cultivating knowledge sharing in and across teams.
Provide data-related technical and architectural assistance to product delivery teams and IT when needed. Assist and support tech leads and senior developers to help unblock issues.
1 month:
Collaborate with key stakeholders (product managers, engineers, architects) to understand the current state of the research data landscape and identify immediate opportunities for improvement.
Document the as-is data/technical landscape for research data and the wider domain.
Begin building relationships and feedback loops with data governance, security, and other relevant groups to ensure alignment on data standards, security policies, and architectural principles.
Start to map out the existing data sources and identify potential issues that need to be addressed.
3 months:
Maintain a high-level roadmap for the development of the research data ecosystem, outlining key milestones and deliverables for the next 6-12 months, and present to senior leadership
Determine how the technical architecture can support delivery autonomy while supporting consistent user journeys across our platforms
Perform feasibility analysis and provide recommendations on Build vs. Buy for systems that support the agile development process, scalability, and data governance requirements
Create an architectural forum to bring together architects and tech leads in the resea
Requirements
Essential:
Proven experience designing, delivering, and scaling data-intensive applications. Demonstrated ability to architect data solutions that meet performance, scalability, and security requirements.
Experience working on transformation projects involving introducing new technologies and ways of working within a business. Ability to drive adoption of new data architectures and technologies.
Ability to clarify and uncover technical requirements, risks, and opportunities with tech leads and collaborators. Experience translating business needs into technical specifications.
Where necessary, advocate for and enforce cross-functional technical and data requirements (e.g., GDPR, security, operability, etc.)
Hands-on experience with cloud data platforms and services (e.g., AWS, Azure, GCP). Familiarity with cloud-native data architectures and technologies.
Desirable:
Experience with data management tools and processes.
Experience with AI and Machine Learning, including MLOps practices.
Experience with decentralised Data Mesh and Data Product architecture principles.
Additional Information
The role is to provide data engineering and architectural leadership to teams building core data products and services powering Springer Nature's researcher brands.
About you
You're happy working across teams and departments to align and coordinate the development and delivery of data-centric solutions. You spot risks and opportunities and fill in the gaps between delivery teams. You help remove blockers throughout the data supply chain. You produce demos and MVPs for analysis and to test out possible solutions that you can use to demonstrate ideas or gaps. You take an iterative approach to solving complex problems and seek feedback to quickly arrive at the best result.
Springernature · London, UK