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. Drive technical architecture decisions for data ingestion, transformation, storage, and serving layers, aligned to medallion architecture patterns, balancing scalability, reliability, and cost. Plan and manage team capacity, balancing project delivery, operational support, and continuous improvement initiatives. Produce and maintain technical documentation, including architecture diagrams, data flows, and operational runbooks. Drive adoption of AI-enabled engineering practices across the team, including AI-assisted code generation, automated testing, documentation, troubleshooting, and operational analytics, to improve team productivity, speed, and quality. Track and improve delivery and operational KPIs, including pipeline reliability, data quality SLA adherence, incident resolution time, and team utilization, driving continuous improvement. Supervisory Responsibilities This role has direct people management responsibilities for data engineers. Responsibilities include hiring, onboarding, performance evaluation, career development, and day-to-day team leadership. Computer Skills Advanced SQL and strong proficiency in Python for data engineering; PySpark experience required, Scala familiarity is a plus. Deep experience with distributed data processing, especially Apache Spark and PySpark for large-scale batch and streaming workloads. Expert-level knowledge of modern data warehouse or lakehouse architecture, including medallion design patterns and the delivery of reusable data products and analytical data products. Hands-on experience with Databricks, including Spark, Delta Lake, Databricks SQL, notebooks, workflows/jobs, repos, cluster management, and production pipeline development; strong familiarity with Unity Catalog for centralized metadata management, fine-grained access control, data lineage, discovery, and permissions governance across workspaces. Hands-on experience with Microsoft Azure data services, including Azure Data Factory (ADF), Azure Storage Accounts (ADLS Gen2/Blob Storage), and strong understanding of Azure security and access control models (RBAC, managed identities) Proficiency with version control and CI/CD tools (e.g., Databricks Asset Bundles, Git, Azure DevOps, GitHub). Strong understanding of data governance, metadata management, data quality frameworks, and security best practices Experience with monitoring, alerting, and observability tools for production data services Familiarity with RPA concepts and automation platforms sufficient to manage delivery outcomes and evaluate solution approaches (deep RPA development expertise is not required) Ability to produce technical architecture documentation using standard documentation and diagramming tools Familiarity with Databricks AI/BI capabilities, including Genie, and understanding of how high-quality data foundations support conversational analytics and business-facing experiences. Experience driving team adoption of AI-powered engineering tools (e.g., copilots, code assistants, automated testing and documentation tools) to improve development productivity and quality. Other Qualifications 8+ years of experience in data engineering, software engineering, or related technical roles, with at least 3 years in a people management or team leadership capacity. Demonstrated ability to build, develop, and retain engineering talent Experience leveraging AI-powered tools (e.g., code assistants, automated testing, documentation or troubleshooting tools) to improve team productivity and engineering quality Strong communication skills and ability to work effectively with cross-functional stakeholders across technical and business teams Strong ability to lead and collaborate in a matrix environment across Beijing, Shanghai, Dalian, and global teams. Experience working in Pharma, BioTech, or large-scale corporate environments is a plus Experience in shared services, global capability centers, or centralized delivery organizations is preferred. Databricks certification preferred (Certified Data Engineer Associate or higher); Azure certification or other relevant modern data engineering credentials are also valued. Good written and spoken English required, with the ability to communicate effectively in technical discussions, produce clear documentation, and participate in global alignment meetings. 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