Data Engineer

About Springer Nature Group 

Springer Nature opens the doors to discovery for researchers, educators, clinicians and other professionals. Every day, around the globe, our imprints, books, journals, platforms and technology solutions reach millions of people. For over 180 years our brands and imprints have been a trusted source of knowledge to these communities and today, more than ever, we see it as our responsibility to ensure that fundamental knowledge can be found, verified, understood and used by our communities – enabling them to improve outcomes, make progress, and benefit the generations that follow. Visit group.springernature.com and follow @SpringerNature / @SpringerNatureGroup 

Who we are 

At Springer Nature AI Labs (SNAIL), we’re shaping the future of scientific publishing through responsible, human-centered AI. Our team is at the forefront of integrating advanced AI technologies to optimize processes and enhance the user experience for researchers and academics worldwide. We value a collaborative work environment where ideas flourish, and innovation is encouraged. With our curiosity-driven, impact-first culture, we focus on delivering AI innovation at scale always with integrity and in close collaboration across functions. Our commitment to long-term growth ensures that our people are nurtured and developed to reach their full potential. 

Who you are 

You are a curious and collaborative data engineer with a passion for designing robust, scalable, and secure data systems that unlock value across the organization. You have a solid understanding of and hands-on experience with cloud-native data engineering. You enjoy solving complex problems, building new data capabilities and turning raw data into powerful re-usable data products. You thrive in cross-functional teams, bringing a strong sense of ownership and technical excellence to everything you do. Whether you're crafting data pipelines or navigating distributed systems you stay committed to delivering high-quality solutions that scale. You understand that great engineering is as much about people and collaboration as it is about code and you enjoy contributing to a learning culture that values shared success. 

What you will do 

As Data Engineer, you will: 

• Build & Optimize Data Pipelines: Design, develop and maintain high-quality, reusable data pipelines across diverse datasets. Work with tools like Postgres, BigQuery, DBT, and Python to deliver structured, reliable data for downstream users and products 

• Collaborate Cross-Functionally: Work closely with AI/ML engineers, and product teams to define data requirements, co-design systems and ensure data solutions align with business goals. 

• Support Data Products at Scale: Take end-to-end ownership of data workflows from ingestion and transformation to monitoring and production support. Ensure availability, performance, and compliance with data privacy standards. 

• Champion Best Practices: Apply agile engineering techniques such as pair programming, TDD, CI/CD, and automation to build maintainable and resilient systems. Contribute to community-of-practice sessions and promote a culture of continuous improvement. 

• Innovate with Purpose: Explore and introduce new tools or methods where appropriate. Stay up-to-date with trends in cloud data engineering and modern architectural patterns like Data Mesh. 

Must-Have Qualifications 

• Proven Experience: solid experience in data/software engineering on cloud platforms (e.g. GCP, AWS, Azure), ideally within a fast-paced or agile environment. 

• Programming Proficiency: High competence in SQL and strong experience with at least one modern programming language (e.g. Python, Java). 

• Continuous Delivery: Comfort with CI/CD pipelines, automated testing, and monitoring for data products. 

• Data Engineering Expertise: Practical knowledge of building, deploying, and maintaining data pipelines and services in production environments. 

• Systems Thinking: Solid understanding of data architecture and distributed systems concepts. 

• Workflow & Tools: Familiarity with tools such as DBT, Airflow, or similar orchestration frameworks. Experience with version control, containerisation, and modern deployment practices. 

• Quality Mindset: Deep understanding of data quality principles and ability to design strategies and tooling to detect and resolve data issues. 

• Team Culture & Collaboration: Experience working in cross-functional agile teams; a strong communicator who can translate technical concepts to non-technical stakeholders. 

• Growth-Oriented: Desire to grow and contribute to a supportive, high-trust team culture. 

By joining Springer Nature, you will actively contribute to the development and implementation of Data and AI solutions that drive the future of scientific publishing. Join us as we pioneer the future of scientific publishing through artificial intelligence. 

Internal applicants: We encourage that you speak to your manager once the interview process has started. At the point of offer acceptance, it is required that you inform your manager. If for any reason you’re unable to do so, please contact HR who can provide guidance as required. 

At Springer Nature we value the diversity of our teams. We recognize the many benefits of a diverse workforce with equitable opportunities for everyone. We strive for an inclusive workplace that empowers all our colleagues to thrive. Our search for the best talent fully encompasses and embraces these values and principles. Springer Nature was awarded Diversity Team of the Year at the 2022 British Diversity Awards. Find out more about our DEI work here https://group.springernature.com/gp/group/taking-responsibility/diversity-equity-inclusion 

For more information about career opportunities in Springer Nature please visit https://careers.springernature.com/ 

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