Location
London

Hours
Full Time

Salary
Competitive, industry-benchmarked salary

About the Role
The Data Solution Architect will provide data engineering and architectural leadership to teams building core data products and services powering Springer Nature's researcher brands. You will coordinate across teams and departments to align and deliver data-centric solutions, spotting risks and opportunities and filling gaps between delivery teams. Your role includes producing demos and MVPs for analysis and testing possible solutions, taking an iterative approach to solving complex problems and seeking feedback to quickly arrive at the best results.

Key responsibilities include coordinating consistent data product development and utilisation, working with Data Architects to understand team needs and constraints, optimising existing data products, advocating for data-as-a-product delivery, and collaborating on the design of the research data ecosystem. You will address disambiguation, data product creation, API development, model building, harmonisation, standardisation, and governance.

Adopting company-standardized cloud technologies and collaborating with technology teams to improve offerings is essential. You will consider the best technology for data teams with varying technical literacy, ensure data security and accessibility in line with regulations such as GDPR, and build relationships across departments to ensure alignment and collaboration.

You will clarify constraints, trade-offs, and decisions to non-technical stakeholders, introduce business and product leaders to data concepts, foster a safe and collaborative technical community, and provide technical and architectural assistance to product delivery teams and IT. Supporting tech leads and senior developers to unblock issues is also part of the role.

In your first month, you will collaborate with stakeholders to understand the current research data landscape, document the as-is state, build relationships with governance and security teams, and map existing data sources. By three months, you will maintain a high-level roadmap for the research data ecosystem, determine how architecture supports delivery autonomy, perform feasibility analyses, and create an architectural forum. By six months, you will refine the roadmap based on feedback, scale successful approaches, and communicate a clear vision aligned with strategic goals.

Strong communication skills in English are important for collaboration and idea exchange in this role.

Experience
- Proven experience designing, delivering, and scaling data-intensive applications
- Demonstrated ability to architect data solutions meeting performance, scalability, and security requirements
- Experience with transformation projects introducing new technologies and ways of working
- Ability to drive adoption of new data architectures and technologies
- Skilled at clarifying and uncovering technical requirements, risks, and opportunities
- Experience translating business needs into technical specifications
- Advocacy for cross-functional technical and data requirements including GDPR, security, and operability
- Deep experience with various database types including relational, NoSQL, graph, vector stores, and data warehouses, especially in cloud environments
- Knowledge of data modelling techniques and data warehousing methodologies such as Kimball, Inmon, and Data Vault
- Hands-on experience with cloud data platforms and services (AWS, Azure, GCP)
- Familiarity with cloud-native data architectures and technologies

About you
- Collaborative and able to work effectively across teams and departments
- Strong communicator with ability to explain complex technical concepts to non-technical stakeholders
- Proactive problem solver who takes an iterative approach and seeks feedback
- Passionate about fostering a collaborative technical community and knowledge sharing

Qualifications
- Relevant experience and skills as outlined above are essential
- Experience with data management tools and processes is desirable
- Experience with AI, Machine Learning, and MLOps practices is a plus
- Familiarity with decentralised Data Mesh and Data Product architecture principles is advantageous