Data Engineer

Who We Are

At Springer Nature AI Labs Data Engineering Team (SNAIL DE), we are working at the heart of Springer Nature’s publishing workflows. The Team is responsible for delivering high-quality data products and business intelligence to our consumers, combining data from various systems. We are a crucial enabler for continuous improvement, and process monitoring. 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 youre 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 a Data Engineer, you will:

• Build & Optimize Data Pipelines: Design, develop and maintain high-quality, reusable data pipelines across diverse datasets. Work with tools like BigQuery, DBT, and Airflow to deliver structured, reliable data for downstream users and products
• 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.
• Enable Self-Service Analytics: Build intuitive, visually compelling reports and dashboards using Looker and Looker Studio. Empower stakeholders by providing training and guidance on how to explore data and create their own insights, fostering a culture of self-service and data-driven decision-making.
• Collaborate Cross-Functionally: Work closely with data product managers, and product teams to define data requirements, co-design systems and ensure data solutions align with business goals.
• 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.
  
  

• Data Architecture and Modelling: knowledge of data architecture and data modelling principles, ideally on cloud platforms (e.g. GCP, AWS, Azure).
• Data Engineering Expertise: Practical knowledge of building, deploying, and maintaining data pipelines and services in production environments. Familiarity with one of the data tools such as dbt, Airflow, Dataflow, Apache Spark.
• Programming Proficiency: High competence in SQL and 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.
• Quality Mindset: Deep understanding of data quality principles and ability to design strategies and tools 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.