Data Engineering Manager

General Description

The Data Engineering Manager in the GTS – Data & Analytics team leads the Dalian-based data engineering function as part of BeiGene's shared services center, owning end-to-end delivery of data pipelines, data products, and analytical data products on the Databricks lakehouse platform that support enterprise analytics, AI, and business operations across China.

This role directly manages a team of engineers and is accountable for platform stability, pipeline reliability, data governance execution, and engineering standards. The manager also oversees the RPA automation program through RPA technical resources, ensuring operational continuity and business value delivery. This role is expected to build an AI-proficient engineering team, driving adoption of AI as a core component of the software development lifecycle — from requirements acceleration and solution design to code generation, testing, documentation, and continuous improvement — while maintaining strong standards for quality, security, and compliance.

The position partners closely with domain stakeholders, BI/reporting, project management, and global technology teams to ensure aligned priorities and predictable execution. It plays a critical role in building a high-performing team capable of delivering modern data engineering solutions leveraging Databricks, medallion architecture, and AI-enabled engineering practices.

Essential Functions of the Job

• Lead, mentor, and develop a team of data engineers, including performance management, career development, skill-building, and day-to-day work allocation.
• Own the end-to-end delivery and operations of enterprise data pipelines and data products on the Databricks lakehouse platform, ensuring stability, data quality, performance, and compliance with governance standards.
• Oversee the RPA automation program through RPA technical resources, ensuring delivery quality, operational monitoring, and continuous improvement.
• Partner with the Project Manager to align engineering priorities with business demand and team capacity.
• Collaborate with business stakeholders on solution design and technical approach for domain-specific data requirements.
• Define and enforce engineering standards, including code review processes, CI/CD practices, documentation requirements, and data governance policies.
• Oversee data platform operations on Databricks and Azure, including workspace administration, Unity Catalog governance, cluster management, cost optimization, performance tuning, and security compliance.
• Ensure data quality by establishing automated validation frameworks, monitoring systems, reconciliation controls, and clearly defined data SLAs.
• Manage incident response for production data services, including escalation, root cause analysis, and implementation of preventive measures.