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YOUR TASKS AND RESPONSIBILITIES

Work in an agile team and collaborate with colleagues all over the world to develop and operate reliable data pipelines from source to product to ensure compliant access and use of data related to the local Product Supply organization
Collaborate closely with relevant stakeholders in China and beyond to capture datarelated business requirements and translate them into efficient technical solutions to ensure a fitforpurpose technical implementation
Work with architects and subject matter experts to capture and manage dependencies between data solutions to ensure a healthy data ecosystem
Actively exchange with peers on bestpractices, technical patterns and solutions and apply them in the local context to build and maintain a consolidated and scalable data landscape for the Product Supply organization in China
Mentor junior engineers and uphold high standards in code review, testing, and documentation of the implemented data solutions in China

WHO YOU ARE

Bachelor’s degree in Computer Science, Data Science, Information Technology, or a related field
5 years of experience in data engineering, data operations, or a similar role, delivering productiongrade data pipelines and services
Strong ownership of deliverables, high standards for code review and peer mentorship, and a commitment to clear documentation, metadata and lineage publishing, and sustainable data engineering standards and practices
Excellent analytical and problemsolving skills
Understanding of requirements related to product supply and manufacturing in the pharmaceutical industry
Good communication and collaboration abilities to work independently in a selforganized team
Fluent in Chinese and English
Willingness and ability to learn and integrate new tools and technologies to enhance work efficiency and effectiveness
Understanding of Data Product concepts and lifecycle management, including data quality key dimensions and tools such as Great Expectations, dbt tests, or Elementary
Handson experience building RetrievalAugmented Generation (RAG) data pipelines, including automated document ingestion, preprocessing, chunking, embedding generation, and loading data into vector stores for efficient retrieval
Familiarity with RAG architectures and components such as vector databases, embedding models, and LLMs; experience designing these systems is a plus
Handson proficiency in SQL, Python, PySpark, or Spark to build scalable data pipelines using modern data processing frameworks such as Databricks or Snowflake
Proven experience with ETL/ELT tools such as Kettle/Pentaho, DataWorks, or similar
Ability to integrate data from source systems using interface patterns such as ODBC, GraphQL, OData, REST, or event streaming
Experience with cost and performance optimization and FinOps practices for cloud data platforms such as Snowflake or Databricks, ensuring reliable production operations
Understanding of modern lakehouse table formats such as Apache Iceberg and Delta Lake, and ability to design, build, maintain and document conceptual, logical and physical data models
Understanding of data architecture patterns such as data hub, data mesh, or data fabric
Experience with GxP and computerized system validation is a plus
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