National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Architect

Springer Nature
London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Solutions Architect (Azure & MS Fabric)

Enterprise Data Architect

Enterprise Data Architect

PLM Data Architect

Data Engineer

This role will develop a cohesive data architecture in a key area across Springer Nature’s research brands, transforming services and products towards a data-driven customer experience.

About you

You bring people together, getting the right artefact in front of the right people to shift the conversation towards agreement and understanding. You learn quickly, taking in the full context and complexity to work out what can and can’t be safely set aside for now. You communicate well and ensure stakeholders understand your architectural vision and its relationship to the business capabilities it will enable. You architect with an iterative approach, actively seeking input from multiple points, gathering feedback and adapting to new requirements and information.

Role Responsibilities

  • Collaborate with business stakeholders, technology teams, and data professionals to define and align on a target data architecture that supports strategic goals.

  • Drive the development and maintenance of data architecture guidelines and standards to ensure consistency across the organization, including digital products and marketing domains.

  • Provide guidance and mentorship to department representatives to promote improved data quality, harmonization, and governance practices.

  • Introduce and explain data concepts to senior business and product leaders to foster data literacy and informed decision-making.

  • Develop and maintain data models and artifacts to document the as-is and to-be states of the customer data landscape.

  • Identify and define desired data products that meet the research organization's needs, ensuring alignment with business requirements.

  • Collaborate with teams and solution architects to contribute to the development of the broader data ecosystem, including capabilities like data disambiguation, APIs, and machine learning models.

  • Continually validate architecture through delivery with product teams and course correct as necessary.

  • Collaborate with data privacy, governance, and management roles to establish and enforce data management, security, and compliance policies within areas of active development, ensuring adherence to relevant regulations (e.g., GDPR).

  • Build and maintain strong relationships with key stakeholders, including Solution Architects, Data Governance, Data Directors, Heads of Product, Data Protection Officer (DPO), Enterprise Architects, and Cybersecurity, to ensure the delivery of reliable, right, and secure data solutions.

  • Collaborate with other data architects in workshops, planning sessions, and product teams to create shared artifacts, fostering a collaborative and consistent approach to data architecture.

Skills & Experience Essential

  • Extensive experience in data modeling, with a proven track record of successfully modeling complex data domains.

  • Demonstrated experience in defining and documenting data strategies, roadmaps, and principles.

  • Strong understanding of data governance principles and practices, with experience driving improvements in data quality and harmonization.

  • Experience in defining and documenting non-functional requirements (e.g., data management, security, compliance) and ensuring their implementation.

  • Ability to review proposed technology options for architectural fit and define appropriate frameworks for technology selection.

  • Experience defining success measures and monitoring key data components to ensure performance and reliability.

  • Excellent communication and interpersonal skills, with the ability to effectively clarify constraints, trade-offs, and essential decisions to technical and non-technical stakeholders.

  • Proven ability to develop strategies to improve data quality and ensure data accuracy and consistency.

  • Experience creating regular feedback loops with stakeholders and product teams to ensure alignment and incorporate learnings into the data architecture.

Desirable

  • Knowledge of architectural disciplines such as data mesh, business intelligence (BI), data warehousing, and data platforms.

  • Experience with cloud-based data solutions and technologies.

  • Strong facilitation and alignment skills, with the ability to effectively navigate and influence across organizational silos.

  • Experience with aligning Agile delivery teams.

What you will be doing

1 month

  • Collaborate with key stakeholders to understand the research data landscape's current state and identify immediate improvement opportunities.

  • Document the as-is data/technical landscape for research data and the broader domain.

  • Build 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 must 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 presenting 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.

6 months

  • Refine the roadmap and architecture based on feedback from initial delivery, 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 organizational goals.

#LI-AR1

#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.