Senior Batch Data Engineer (Alibaba Cloud Stack)

Role Overview

Pipeline Architecture and Modeling

• Design, build, and maintain scalable ETL/ELT batch pipelines using Alibaba Cloud DataWorks and MaxCompute.
• Build ODS, DWD, DWS, and ADS enterprise warehouse layers with modular SQL and Python scripts.
• Develop ingestion, transformation, and batch workflows across MaxCompute, Hologres, DataWorks, DTS, Lakehouse, and warehouse architectures.
• Ensure reliability, query performance, schema stability, and cost-effective cloud resource usage.

AI Engineering and Testing

• Use Claude, GitHub Copilot, prompt engineering, and related AI tools to accelerate code generation, SQL refactoring, and data validation.
• Write unit tests, simulate data scenarios, and audit data accuracy before production release.

Data Quality and Monitoring

• Implement data quality rules, automated reconciliation scripts, and validation checks inside DataWorks jobs.
• Build Lark-integrated monitoring and alerting workflows for data drift, schema breaks, and pipeline failures.
• Provide clean and trusted datasets for analytics, BI reporting, and AI/LLM services.

Ownership and Collaboration

• Own daily T-1 pipeline stability, job scheduling, historical backfills, and legacy issue cleanup.
• Collaborate fluently in English and Chinese with technical leads, product managers, and developers.

Required Qualifications

• At least 5 years of hands-on expertise with Alibaba Cloud data services and MaxCompute Lakehouse architectures.
• Advanced SQL and Python proficiency for data engineering, performance tuning, and database optimization.
• Experience modeling ODS, DWD, DWS, and ADS enterprise data structures and managing batch Lakehouse architectures.
• Fluent spoken and written English and Chinese.
• Strong ownership, proactive communication, high velocity, and a deliver-fast, iterate-continuously approach.

Preferred Qualifications

• Experience using AI coding assistants such as Copilot or LLMs for ETL development, code reviews, and query optimization.
• Experience integrating alerting and monitoring with Lark or a similar collaboration platform.
• Experience applying AI-driven testing methodologies before production release.