At a Glance
- Tasks: Lead data engineering and architecture for innovative research data products.
- Company: Join a leading global publisher with a focus on collaboration and innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a strong emphasis on knowledge sharing and collaboration.
- Why this job: Shape the future of research data and make a real impact in the academic world.
- Qualifications: Experience in data architecture and a passion for solving complex problems.
The predicted salary is between 60000 - 80000 £ per year.
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.
Role Responsibilities
- 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‑standardised 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).
- 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
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.).
- Deep, demonstratable 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.
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.
What you will be doing
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 versus 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.
6 months
- 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.
Having a good command of English is important; collaboration is important in our day‑to‑day work, so being able to communicate your ideas and understand others is key.
For all roles in all locations, we offer a competitive, industry‑benchmarked salary.
Data Solution Architect in London employer: Dormont Manufacturing Co
Springer Nature is an exceptional employer that fosters a collaborative and innovative work culture, particularly for the Data Solution Architect role. With a strong emphasis on employee growth, you will have the opportunity to lead transformative data projects while working alongside diverse teams in a supportive environment. The company offers competitive salaries and encourages knowledge sharing, making it an ideal place for professionals looking to make a meaningful impact in the research data landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Data Solution Architect in London
✨Tip Number 1
Network like a pro! Reach out to people in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or demo of your past projects. This is your chance to shine and demonstrate how you can solve complex problems, just like the role requires.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data architecture. We want you to feel confident discussing your experience and how it aligns with the job.
✨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 in London
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 engineering and architectural leadership, and don’t forget to mention any relevant projects that showcase your skills in building data products.
Showcase Collaboration Skills:Since this role involves working across teams, emphasise your ability to collaborate and communicate effectively. Share examples of how you've coordinated with different departments to deliver data-centric solutions and overcome challenges.
Demonstrate Problem-Solving Abilities:We love candidates who take an iterative approach to problem-solving. Include specific instances where you’ve identified risks or opportunities and how you’ve worked to fill gaps in delivery teams. This will show us your proactive mindset!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter.
How to prepare for a job interview at Dormont Manufacturing Co
✨Know Your Data Architecture
Make sure you understand the key concepts of data architecture and how they apply to the role. Brush up on your knowledge of cloud platforms, data modelling techniques, and the specific technologies mentioned in the job description. Being able to discuss these confidently will show that you're serious about the position.
✨Showcase Your Collaboration Skills
Since this role involves working across teams, be prepared to share examples of how you've successfully collaborated with different departments in the past. Highlight any experiences where you’ve helped remove blockers or facilitated communication between technical and non-technical stakeholders.
✨Prepare for Technical Questions
Expect to face technical questions related to data solutions and architectures. Review common challenges in data engineering and think about how you would approach them. Be ready to discuss your experience with various databases and how you’ve implemented data governance and security measures.
✨Demonstrate Your Problem-Solving Approach
The role requires an iterative approach to solving complex problems. Prepare to discuss specific examples where you’ve identified risks and opportunities, and how you’ve used feedback to refine your solutions. This will illustrate your ability to adapt and improve processes over time.
