Data Engineer - Bilingual Mandarin required

Basic Information

Work Authorization: Green Card / U.S. Citizen required (we do not sponsor)

Job Title: Data Engineer

Focus Areas: Data ingestion, data lakehouse, data warehouse, data platform, data service APIs, data quality & engineering agent development

Level: Junior to mid-level with high growth potential

Location: CA or NC: remote, or hybrid (per company requirements)

Employment Type: Full-time

Language: English required; Mandarin is a strong plus

Cross-Timezone Work: Must maintain a regular collaboration window with teams in other countries; strong async communication and documentation skills required (approx. 2 hrs/day overlap needed)

Role Positioning

CWILL is building data infrastructure to support business operations, product capabilities, customer service, analytics, and intelligent applications. As a US-side data engineer, you will participate in multi-source data ingestion, data lakehouse and warehouse development, data quality governance, data platform capability building, and AI Agent engineering automation exploration.

We are looking for candidates with a solid foundation in SQL, Python, and data engineering — someone who can, with guidance from the existing data team, progressively take ownership of data ingestion, modeling, quality, and service tasks, while collaborating effectively with domestic data engineering, analytics, and business teams.

This is not a pure data analysis, BI reporting, or one-off scripting role. It is a comprehensive data engineering position focused on data integration, data warehouse development, data platform capabilities, data services, and engineering automation.

Role Mission

Through stable, well-structured, and scalable data engineering capabilities, help the company unify, govern, model, and serve data scattered across business systems, SaaS platforms, external channels, and internal systems — improving the usability, accuracy, timeliness, and reusability of CWILL's data assets.

Key Responsibilities

• Data Ingestion & Pipeline Development
• Handle data collection, sync, cleansing, and loading
• Build and maintain data models
• Optimize data organization and query performance on OLAP engines
• Build and maintain data quality rules
• Support data validation, anomaly detection, alerting, and issue resolution
• Contribute to data governance capabilities
• Support the data service layer by delivering standardized APIs, metric services, and data capabilities
• Explore AI agent applications in data development, governance, quality detection, task operations, anomaly diagnosis, and documentation generation

Requirements

• Must-Have Experience:
• 1-4 years of experience in data engineering, data platforms, data warehousing, backend development, analytics engineering, or a related role
• Real project experience in data ingestion, data pipelines, data warehouse development, data modeling, data services, or data platform work
• Strong learning ability and execution skills; able to independently drive small-to-medium data engineering tasks with clear objectives
• SQL Skills: Proficient in SQL for querying, cleansing, aggregation, deduplication, comparison, validation, and metric calculation
• Java or Python for API integration, data processing, automation scripting, and file handling
• Familiar with engineering patterns: REST APIs, OAuth/API keys, pagination, rate limiting, retry logic, error handling, logging, and task idempotency
• Good code structure habits; writes clean, maintainable, and reusable code
• Familiar with Git, code review practices, README documentation, logging, testing, and collaborative engineering workflows
• Pipeline & Platform Tools: Familiar with one or more of: SeaTunnel, Kafka, Flink, Spark (data integration, real-time, or offline processing)
• Doris, ClickHouse, Snowflake, BigQuery, Redshift, Databricks, PostgreSQL (data warehouse, OLAP, or lakehouse systems)
• DolphinScheduler, StreamPark, Airflow, Dagster, Prefect, dbt (scheduling, development, or task management tools)
• Understands data pipeline operations: scheduling, dependencies, monitoring, failure retry, backfill, version management, and deployment processes

Nice-to-Have Experience

• Integrating third-party SaaS data: CRM, ERP, marketing platforms, customer service systems, logistics, e-commerce, payment systems, or ad platforms
• Building data lakehouses, data middle platforms, data platforms, or enterprise-level data warehouses
• Developing data service APIs, metric services, internal data products, or lightweight backend services
• Experience with data quality frameworks, data lineage, metadata management, data catalogs, observability, or monitoring and alerting
• AWS, GCP, or Azure cloud platform experience
• Docker, CI/CD, Terraform, Kubernetes, or basic DevOps experience
• Experience with LLMs, AI Agents, code generation, automated testing, task inspection, data quality agents, or engineering efficiency tooling
• Experience with cross-border teams, international business, supply chain, e-commerce, logistics, marketing, or customer success data scenarios

Benefits

Starting Pay: $75,000 - $100,000 depends on experiences, open to negotiation

401(k)

PTO

Paid Holidays

Insurance