Senior Data Engineer, Content Management Systems

Are you excited to design and operate large‑scale data platforms that power global research and innovation?

Do you enjoy collaborating across teams to build reliable, compliant, cloud‑based data solutions?

Elsevier is a global information analytics company that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. We serve the research, academic, and clinical communities through the application of technology and analytics to content. Our portfolio of solutions brings together extensive Scientific, Technical and Medical content, powerful analytics, and technologies. We help our corporate customers, R&D professionals, engineers, scientists, and commercial marketing leaders, to innovate and commercialize ideas, products, and processes.

This role is responsible for designing, delivering and operating China‑local content management and data processing systems. You will work close to code, data pipelines, and production systems, solving real implementation problems related to scale, data quality, operability, and compliance in the China environment. The position works from designs and requirements provided by Product Solution Architect and Product teams, collaborate closely with business stakeholders and engineering teams to translate requirements into technical solutions.

A key objective is to ensure high standards of data quality, performance, and governance. You will support integration across enterprise systems and third‑party platforms. Overall, you will play a critical role in enabling data‑driven transformation from global products’ offering to integrate with local solutions and serve China customers’ needs.

• Design, implement, test, and maintain production‑grade backend services and data pipelines in cloud environment
• Write clean, maintainable, and efficient code for large‑scale content ingestion, processing, and delivery
• Debug and resolve complex issues including data correctness, performance bottlenecks, and operational failures
• Participate in code reviews and contribute to shared libraries, reusable components, and internal tooling
• Build and operate pipelines processing high‑volume scholarly content, including XML and other semi‑structured formats
• Implement metadata extraction, transformation, validation, deduplication, and enrichment logic
• Handle imperfect inputs such as non‑standard schemas, missing fields, inconsistent source data, with encoding issues
• Implement ETL/ELT processes for structured and unstructured data
• Integrate internal systems with third‑party platforms via APIs and middleware
• Implement data quality checks, automated testing, and observability to ensure data quality, consistency, and performance
• Optimize data pipelines and system performance across systems
• Implement automated workflows for content processing, validation, and policy enforcement
• Reduce manual operational steps through engineering automation
• Ensure compliance with China regulatory requirements, including Cybersecurity Law and PIPL
• Support audits through technical controls such as logging, lineage tracking, and access trails
• Collaborate with cross‑functional teams and stakeholders
• Support system migrations, upgrades, and cloud adoption
• Monitor and troubleshoot data and integration issues
• Maintain documentation for data architecture and workflows
• Investigate incidents, perform root‑cause analysis, and implement permanent fixes

• 5+ years of hands‑on experience as a software engineer on backend or data‑intensive systems
• Bachelor's degree or above in Computer Science, Software Engineering, or a related field.
• Strong understanding of software engineering fundamentals: data structures, system design basics, testing, and debugging
• Advanced relational database expertise and data warehouse project experience

• Strong programming experience in Python or Java
• Advanced SQL and relational database expertise
• Hands‑on experience with Snowflake for data warehousing
• Hands‑on experience with ETL/ELT pipelines
• Experience with cloud platforms (AWS, or Alibaba Cloud)
• Hands‑on experience with Databricks, Spark and…