Principal Data Engineer

About the role

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 i terativ e approach to solving complex problems and seek feedback to quickly arrive at the best result .

Role R esponsibilities :

• Coordinate across teams to ensure consistent data product development and utili s ation, 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 ecosystem s .

• Work with both data producers and consumers to optimi s e 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, harmoni s ation, standardi s ation, and governance.

• Adopt company-standardized 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, a dvocate 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 model l ing techniques and data warehousing methodologies (e.g., Kimball , I nmon , 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 will you 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 presen t 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 a n architectural forum to bring together a rchitects and t ech l eads 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 success ful approach es 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 organi s ational goals

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