Data Engineer (Data Platform)

About Us

We're a world-leading smart mobility SaaS tech company with almost 2,300,000 active users. Our teams are collaborative, vibrant, and fast-growing, and all team members are empowered with the freedom to influence our products and technology.

Are you curious, innovative, and passionate? Do you take ownership, embrace challenges, and love problem-solving?

Your Role

• Design, build, and maintain data pipelines and ETL workflows using tools like Airflow.
• Develop monitoring systems to detect upstream data changes and maintain data freshness.
• Optimize relational and analytical databases for performance, including partitioning, indexing, and table design.
• Implement data quality, data lineage, and source documentation standards.
• Manage and tune analytics databases such as ClickHouse, Druid, StarRocks, or Doris.
• Work with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Leverage distributed query tools (e.g., Spark, Trino) to support scalable data analysis.
• Build, deploy, and monitor services in Linux environments, including shell scripting and debugging.
• Create and maintain Docker images, ensuring consistent environments for development and deployment.
• Integrate data workflows with CI/CD pipelines using tools such as GitLab, GitHub, or Bitbucket.

Your Qualifications

Core Technical Skills

• Strong proficiency in one or more of the following: C, C++, C#, Go, or Rust.
• Proficiency in Python for scripting and automation.
• Experience with Airflow and complex ETL processes involving large datasets.
• Solid understanding of data architecture, database optimization, and monitoring best practices.
• Hands-on experience with Docker, Linux environments, and version control systems.

Preferred / Nice-to-Have Skills

• Experience or interest in Kubernetes and Helm charts.
• Familiarity with AI/LLM data pipelines — generating tables and views optimized for low-latency queries.
• Experience with LLM Agent development, RAG workflows, or related frameworks such as LlamaIndex, LangGraph, or FastAPI.
• Knowledge of stream processing systems to accelerate data ingestion into analytics databases.
• Exposure to feature engineering and table design in collaboration with data scientists.

Qualifications & Experience

• Bachelor’s Degree or Advanced Diploma in Computer Science, Information Technology, or Engineering, with 3–5 years of experience in a software or technology environment focused on data systems architecture, deployment, and monitoring.
• Candidates without a degree must have 6–10 years of equivalent experience in data engineering, infrastructure management, or backend systems development.