Data Architect

Springer Nature
London
5 days ago
Create job alert

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

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect Manager

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.

Common Pitfalls Machine Learning Job Seekers Face and How to Avoid Them

Machine learning has emerged as one of the most sought-after fields in technology, with companies across industries—from retail and healthcare to finance and manufacturing—embracing data-driven solutions at an unprecedented pace. In the UK, the demand for skilled ML professionals continues to soar, and opportunities in this domain are abundant. Yet, amid this growing market, competition for machine learning jobs can be fierce. Prospective employers set a high bar: they seek candidates with not just theoretical understanding, but also strong practical skills, business sense, and an aptitude for effective communication. Whether you’re a recent graduate, a data scientist transitioning into machine learning, or a seasoned developer pivoting your career, it’s essential to avoid common mistakes that may hinder your prospects. This blog post explores the pitfalls frequently encountered by machine learning job seekers, and offers actionable guidance on how to steer clear of them. If you’re looking for roles in this thriving sector, don’t forget to check out Machine Learning Jobs for the latest vacancies across the UK. In this article, we’ll break down these pitfalls to help you refine your approach in applications, interviews, and career development. By taking on board these insights, you can significantly enhance your employability, stand out from the competition, and secure a rewarding position in the world of machine learning.