Senior Data Engineer

General Description The Senior Data Engineer in the GTS – Data & Analytics team will be based in Dalian, China as part of our shared service center strategy and will help build a modern, scalable data engineering capability that serves global business needs. This role is responsible for designing, building, and operating scalable, reliable, and compliant data pipelines, data products, and analytical data products on the Databricks lakehouse platform to support enterprise analytics, AI, and decision support. This role partners closely with analytics engineers, data scientists, platform teams, and business stakeholders to translate business requirements into resilient, reusable, and governed data assets. The ideal candidate combines strong hands-on engineering depth in Databricks, Spark, Python, and SQL with a practical product mindset and the ability to deliver reliable, consumption-ready data solutions in collaboration with global teams. The position plays a critical role in ensuring data consistency, availability, performance, and security across platforms, with a strong focus on operational excellence and data governance. We are building an AI-proficient data organization, and this role is expected to use AI as a core component of the software development lifecycle—from requirements acceleration and solution design to code generation, testing, documentation, troubleshooting, and continuous improvement—while maintaining strong standards for quality, security, and compliance. Essential Functions of the Job Design, develop, and maintain end-to-end batch and/or streaming data pipelines on the Databricks lakehouse platform to deliver trusted, analytics-ready data products aligned to medallion architecture. Integrate data from multiple source systems, translating business requirements into resilient, reusable technical solutions with clear data contracts and schema governance. Build and manage scalable data models and curated data layers following medallion architecture patterns (bronze/silver/gold) to ensure consistent business definitions, metrics, and governed consumption by downstream BI, analytics, and AI/BI use cases. Develop and maintain data solutions and interfaces for downstream use cases including reporting, self-service analytics, and emerging conversational analytics / AI/BI experiences, ensuring data availability, stability, and controlled evolution of schemas and metrics. Operate and support production data services, including monitoring, alerting, incident resolution, root cause analysis, and release management. Ensure data quality by implementing automated validation, monitoring, reconciliation controls, and clearly defined data SLAs with structured issue resolution processes. Implement and enforce data governance requirements, including metadata management via Unity Catalog, data lineage, discovery, access control, approval workflows, and auditability. Optimize data pipeline performance, reliability, and cost through efficient Spark/PySpark data processing, Delta Lake storage design, partitioning strategies, and query tuning. Conduct design and code reviews, providing technical guidance and mentoring to junior engineers and external resources. Produce and maintain clear technical documentation, including architecture diagrams, data flows, data contracts, semantic documentation, and operational runbooks. Build and maintain high-quality, governed data foundations that support downstream conversational analytics, AI/BI experiences, and natural language querying through well-structured metadata, business definitions, and semantic context. Leverage AI-powered tools (e.g., code assistants, automated testing, documentation and troubleshooting tools) as a core part of the development lifecycle to improve productivity, code quality, and solution reliability. Supervisory Responsibilities This role does not typically have direct people management responsibilities but provides technical leadership and guidance to junior engineers and external or project-based resources as required. Computer Skills Advanced SQL and strong proficiency in Python; PySpark experience required, with Scala as a plus Strong hands-on experience with distributed data processing frameworks, especially Apache Spark and PySpark, for large-scale batch and streaming workloads Strong understanding of modern data warehouse and lakehouse architecture, including medallion design patterns and the delivery of reusable data products and analytical data products Deep hands-on experience with Databricks for enterprise data engineering, including Delta Lake, Databricks SQL, notebooks, workflows/jobs, repos, cluster or compute 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 Familiarity with Databricks AI/BI capabilities, including Genie Code and Genie Spaces, and the ability to help create governed, high-quality data foundations that support natural language and business-facing analytical experiences Hands-on experience with Microsoft Azure data services, including Azure Data Factory, ADLS Gen2/Blob Storage, Key Vault, and strong understanding of Azure security and access control models such as RBAC and managed identities to support data permissions governance and compliance Proficiency with version control and CI/CD tools (e.g., Databricks bundles, Git, Azure DevOps, GitHub) Understanding of cloud data platforms and security concepts, including role-based access control Experience with data quality, monitoring, and observability tools Ability to document technical solutions using standard documentation and diagramming tools Other Qualifications 5+ years of experience in data engineering, software engineering, or related technical roles Experience leveraging AI-powered tools (e.g., code assistants, automated testing, documentation or troubleshooting tools) to improve development efficiency, code quality, and ongoing maintenance of data engineering projects Strong communication skills and ability to work effectively with cross-functional stakeholders Experience working in Pharma, BioTech or large-scale corporate environments is a plus Databricks certification is preferred, especially [Databricks Certified Data Engineer Associate]() or higher; Azure certification or other relevant modern data engineering credentials are also valued Good written and spoken English communication skills are required, with the ability to collaborate clearly and confidently in global technical discussions and produce high-quality technical documentation. Travel Occasional travel may be required, up to 10%, depending on business needs. 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