深圳外企：Senior Data engineer （可以非金融行业，年薪可到 100W ，英语口语流利）
微信：fancyfrees
Senior Data Engineer
2. Required Qualifications
· Education: Bachelor's or master's degree in computer science, Data Science, Software Engineering, Information Technology, or related technical field.
· Experience: 5+ years of professional experience in data engineering, data operations, or a similar role, with a proven track record of delivering production-grade data pipelines, services, and scalable data solutions in enterprise environments.
· Programming & Data Processing: Strong hands-on proficiency in SQL and Python, with solid experience using PySpark, Spark SQL, Scala, or Java to build scalable data processing workflows.
· Platform & Pipeline Engineering: Strong hands-on experience with modern data processing and
orchestration technologies such as Spark and Airflow. Familiarity with streaming or messaging technologies such as Kafka, Flink, or similar platforms is preferred. Exposure to enterprise data platforms such as Databricks, Snowflake, Microsoft Fabric, or equivalent environments is advantageous.
· Data Quality & Data Product Mindset: Strong understanding of data product concepts, lifecycle management, and core data quality dimensions. Experience with data testing, validation, and observability practices using tools such as dbt tests, Great Expectations, Elementary, or equivalent approaches is a strong advantage.
· Data Processing Breadth: Exposure to structured, semi-structured, or unstructured data workflows, including document processing, transformation, metadata handling, or efficient retrieval patterns, is an advantage.
· Data Architecture & Modeling: Good understanding of lakehouse concepts, modern table formats such as Apache Iceberg or Delta Lake, and the ability to design, build, maintain, and document conceptual, logical, and physical data models.
· Ownership & Collaboration: Demonstrated ownership of deliverables, strong standards for code review and documentation, excellent analytical and problem-solving skills, and the ability to work effectively in a self-organized, cross-functional team.
· Language: Professional working proficiency in both Chinese and English, with the ability to collaborate effectively across local and global teams.
· Learning Agility: Willingness and ability to quickly learn and apply new tools, patterns, and technologies to improve engineering efficiency, reliability, and business impact.
3. Preferred Skills
· Data Platform Tooling: Hands-on experience with data governance, catalog, metadata, lineage, or related platform tooling that supports enterprise data products and scalable data operations.
· Domain Knowledge: Experience working with insurance, financial services, or other regulated enterprise data domains. Familiarity with policy, claims, underwriting, customer, risk, or finance-related data is advantageous.
微信：fancyfrees
Senior Data Engineer
2. Required Qualifications
· Education: Bachelor's or master's degree in computer science, Data Science, Software Engineering, Information Technology, or related technical field.
· Experience: 5+ years of professional experience in data engineering, data operations, or a similar role, with a proven track record of delivering production-grade data pipelines, services, and scalable data solutions in enterprise environments.
· Programming & Data Processing: Strong hands-on proficiency in SQL and Python, with solid experience using PySpark, Spark SQL, Scala, or Java to build scalable data processing workflows.
· Platform & Pipeline Engineering: Strong hands-on experience with modern data processing and
orchestration technologies such as Spark and Airflow. Familiarity with streaming or messaging technologies such as Kafka, Flink, or similar platforms is preferred. Exposure to enterprise data platforms such as Databricks, Snowflake, Microsoft Fabric, or equivalent environments is advantageous.
· Data Quality & Data Product Mindset: Strong understanding of data product concepts, lifecycle management, and core data quality dimensions. Experience with data testing, validation, and observability practices using tools such as dbt tests, Great Expectations, Elementary, or equivalent approaches is a strong advantage.
· Data Processing Breadth: Exposure to structured, semi-structured, or unstructured data workflows, including document processing, transformation, metadata handling, or efficient retrieval patterns, is an advantage.
· Data Architecture & Modeling: Good understanding of lakehouse concepts, modern table formats such as Apache Iceberg or Delta Lake, and the ability to design, build, maintain, and document conceptual, logical, and physical data models.
· Ownership & Collaboration: Demonstrated ownership of deliverables, strong standards for code review and documentation, excellent analytical and problem-solving skills, and the ability to work effectively in a self-organized, cross-functional team.
· Language: Professional working proficiency in both Chinese and English, with the ability to collaborate effectively across local and global teams.
· Learning Agility: Willingness and ability to quickly learn and apply new tools, patterns, and technologies to improve engineering efficiency, reliability, and business impact.
3. Preferred Skills
· Data Platform Tooling: Hands-on experience with data governance, catalog, metadata, lineage, or related platform tooling that supports enterprise data products and scalable data operations.
· Domain Knowledge: Experience working with insurance, financial services, or other regulated enterprise data domains. Familiarity with policy, claims, underwriting, customer, risk, or finance-related data is advantageous.
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