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For Companies At a Glance
- Tasks: Lead data engineering and architecture for innovative research data solutions.
- Company: Join a dynamic global leader in research and education publishing.
- Benefits: Competitive salary, opportunities for growth, and a collaborative work culture.
- Why this job: Make a real impact on the future of research data ecosystems.
- Qualifications: Experience in data-intensive applications and cloud platforms is essential.
- Other info: Be part of a progressive team that values curiosity and innovation.
The predicted salary is between 48000 - 84000 £ per year.
What you will be doing
- 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.
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.
Role Responsibilities:
- Coordinate across teams to ensure consistent data product development and utilisation, establishing and delivering the defined data architecture.
- 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-standardised technology, including cloud platforms, and collaborate with technology teams to improve offerings.
- Work with data privacy and governance teams to ensure data security and appropriate accessibility, adhering to relevant regulations (e.g., GDPR).
- Build relationships with other departments/disciplines/groups to ensure alignment and collaboration.
- Clarify constraints, trade-offs, or important decisions to non-technical stakeholders.
- Introduce business and product leaders to data and data engineering concepts and align solutions to user and business needs.
- 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.
Skills & Experience
- 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.).
- Deep, demonstrable experience delivering with various types of databases and design, including relational databases, NoSQL databases, graph databases, vector stores, and data warehouses, particularly in cloud environments.
- Experience with data modelling techniques and data warehousing methodologies (e.g., Kimball, Inmon, Data Vault).
- Hands-on experience with cloud data platforms and services (e.g., AWS, Azure, GCP).
- Familiarity with cloud-native data architectures and technologies.
- 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.
- 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 research data initiatives.
- Based on feedback from initial delivery, refine the roadmap and architecture, incorporating lessons learned and adjusting priorities as needed.
- Scale the successful approaches to other areas of the research data ecosystem, empowering teams.
- Develop and communicate a clear vision for the future of the research data ecosystem, highlighting its role in supporting strategic organisational goals.
We are an ambitious and dynamic organisation, and home to some of the best-known names in research, educational and professional publishing. Working at the heart of a changing industry, we are always looking for great people who care about delivering quality to our customers and the communities we work alongside. In return, you will find that we open the doors to discovery for all our employees – offering opportunities to learn from some of the best in the business, with a culture that encourages curiosity and empowers people to find solutions and act on their instincts. Whether you are at the beginning of your career or are an experienced professional, we invite you to find out more about the roles we offer and explore our current vacancies.
We are a global and progressive business, founded on a heritage of trusted and respected brands – including Springer, founded in 1842, Macmillan, founded in 1843 and Nature, first published in 1869. Nearly two centuries of progress and advancement in science and education have helped shape the business we are today. Research and learning continues to be the cornerstone of progress, and we will continue to open doors to discovery through trusted brands and innovative products and services. Springer Nature Group was created in May 2015 through the combination of Nature Publishing Group, Macmillan Education and Springer Science+Business Media.
Data Solution Architect employer: Springer Nature group
Contact Detail:
Springer Nature group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Solution Architect
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at industry events. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or demo of your work, especially if you’ve got cool data projects. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data architecture. We want you to feel confident and ready to showcase your expertise!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Data Solution Architect
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Data Solution Architect role. Highlight your experience with data-intensive applications and how you've collaborated across teams to deliver data-centric solutions.
Showcase Your Skills: Don’t just list your skills; demonstrate them! Use specific examples from your past work that showcase your ability to architect data solutions and drive adoption of new technologies. This will help us see how you can fit into our team.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate straightforward communication, so make sure your ideas come across without unnecessary jargon or fluff.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, you’ll find all the details about the position there!
How to prepare for a job interview at Springer Nature group
✨Know Your Data Landscape
Before the interview, take some time to research and understand the current state of data solutions in the industry. Familiarise yourself with common challenges and opportunities in data architecture, especially those relevant to the role. This will help you speak confidently about how you can contribute to improving the data landscape.
✨Showcase Your Collaboration Skills
Since this role involves working across teams, be prepared to discuss your experience collaborating with product managers, engineers, and other stakeholders. Share specific examples of how you've built relationships and facilitated communication to drive projects forward. Highlighting your teamwork skills will demonstrate that you're a good fit for their collaborative culture.
✨Prepare for Technical Questions
Brush up on your technical knowledge related to data architectures, databases, and cloud platforms. Be ready to explain your approach to designing scalable and secure data solutions. You might also want to prepare for scenario-based questions where you’ll need to outline how you would tackle specific data challenges.
✨Emphasise Your Problem-Solving Approach
The role requires an iterative approach to solving complex problems. Be ready to discuss how you identify risks and opportunities, and how you’ve used feedback to refine your solutions in the past. Sharing your thought process will show that you can adapt and improve based on real-world insights.