Junior Data Engineer

General Description The Junior Data Engineer in the GTS – Data & Analytics team, based in Dalian as part of BeiGene's shared services center, supports the development, operation, and maintenance of enterprise data pipelines and data assets on the Databricks lakehouse platform used for analytics and reporting. This role works closely with senior data engineers, analytics engineers, BI developers, and business stakeholders across Beijing, Shanghai, and Dalian to help translate data requirements into reliable, governed, and well-structured datasets aligned to medallion architecture standards. The position focuses on learning and applying data engineering best practices and AI-assisted development workflows while contributing to data quality, availability, and operational stability across the data platform. This role is expected to actively adopt AI-powered tools as part of daily engineering work — including code generation, testing, documentation, and troubleshooting — to accelerate learning and improve productivity. Essential Functions of the Job Assist in the development and maintenance of batch and/or streaming data pipelines on Databricks, including working with Delta Lake, notebooks, and scheduled workflows, under guidance from senior engineers. Support data ingestion and integration from multiple source systems into curated data layers following medallion architecture patterns (bronze/silver/gold). Perform basic data transformations and validations to prepare analytics-ready datasets Monitor scheduled data jobs and assist with troubleshooting data pipeline failures Participate in data quality checks, reconciliation activities, and issue resolution Follow established data governance, security, and access control standards Support documentation of data flows, data models, and operational procedures Collaborate with BI developers and analysts to understand downstream data consumption needs, including reporting, dashboard, and emerging AI/BI consumption patterns. Leverage AI-powered tools (e.g., code assistants, automated testing, documentation tools) as part of daily engineering work to accelerate development, improve code quality, and support continuous learning. Supervisory Responsibilities This role has no direct supervisory responsibilities. Computer Skills Solid working knowledge of SQL for data querying, validation, and basic analytical tasks. Basic proficiency in Python with exposure to PySpark for data processing tasks. Understanding of distributed data processing concepts, especially Apache Spark and PySpark. Experience or coursework involving modern data lakehouse architecture, including familiarity with medallion design patterns. Familiarity with Databricks, including notebooks, Databricks SQL, Delta Lake, and basic workflow scheduling; understanding of Unity Catalog concepts is a plus. Basic understanding of version control and CI/CD concepts (e.g., Git, Azure DevOps). Willingness and ability to learn enterprise data platforms and engineering standards Willingness to adopt AI-powered development tools (e.g., code assistants, copilots) as part of the engineering workflow. Other Qualifications 1–3 years of experience in data engineering, analytics, or related technical roles (or strong internship experience) Foundational understanding of data concepts such as schemas, data quality, and data validation Strong problem-solving skills and attention to detail Ability to learn quickly and follow established engineering standards and processes Good communication skills and ability to work effectively in a team environment Eagerness to learn and adopt AI-assisted development practices as a core part of daily engineering work. Databricks Certified Data Engineer Associate certification is a plus. Good written and spoken English communication skills required, with the ability to collaborate clearly in technical discussions and produce high-quality technical documentation. 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