Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Architect

Springer Nature
London
7 months ago
Applications closed

Related Jobs

View all jobs

Lead Architect - Data Engineering

Lead Data & Machine Learning Architect

Lead Data & Machine Learning Architect

Data Science Manager - Marketing & Customer Generative AI Enterprise Architect

Senior Data Engineer / Architect

Lead Data & Machine Learning Architect

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

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.