Remote Data Engineer - Bilingual Mandarin required

Description CWILL (pronounced "quill") is a post-purchase and retention suite built for Shopify brands. Reduce support tickets, recover lost revenue from returns, and turn one-time buyers into loyal fans — with tools purpose-built for every touchpoint that follows the sale. Learn more: www.cwill.com I.

Basic Information Work Authorization Green Card / U.S. Citizen required (we do nor sponsor) Job Title Data Engineer Focus Areas Data ingestion, data lakehouse, data warehouse, data platform, data service APIs, data quality Mandarin is a strong plus Cross-Timezone Work Must maintain a regular collaboration window with teams in other country; strong async communication and documentation skills required (approx. 2 hrs/day overlap needed) II. 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. III. 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.

This role is expected to continuously drive: • More standardized data source ingestion • Clearer data lakehouse and warehouse structure • More automated data quality monitoring • More platform-driven data service capabilities • Progressive adoption of agent-based and automated approaches for data development, troubleshooting, documentation, and quality checks IV. Key Responsibilities 1. Data Ingestion handle data collection, sync, cleansing, and loading • Participate in building offline and real-time data pipelines using SeaTunnel, Kafka, Flink, Spark, or similar technologies to improve ingestion stability and processing efficiency • Handle practical challenges in data sync: authentication, pagination, rate limiting, failure retry, incremental sync, backfill, schema changes, and task anomalies 2.

Data Warehouse build and maintain data models • Support business domain modeling, metric standardization, shared data model development, and core table maintenance • Optimize data organization and query performance on OLAP engines such as Doris to provide stable data support for product, operations, growth, customer success, and management analytics 3. Data Quality ensure data accuracy, completeness, consistency, and timeliness • Participate in data validation, anomaly detection, alerting, and issue resolution; help improve stability of critical data pipelines • Contribute to data governance capabilities including DataHub or similar tools; improve metadata management, data lineage, data asset catalog, and data standards 4. Data Platform ensure data availability for BI dashboards, metric boards, and business monitoring 5.

AI Agent 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 • Familiar with joins, window functions, CTEs, aggregation analysis, incremental logic, and basic performance optimization • Understands data warehouse layering concepts: fact tables, dimension tables, subject domains, metric definitions, and shared models Data Development • Proficient in Java or Python for API integration, data processing, automation scripting, and file handling • Understands common 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 experience with DataHub or similar platforms is a plus Collaboration strong written and spoken English communication skills • Willing to participate in regular fixed collaboration sessions with China-based teams and drive work through documentation and async communication Nice-to-Have • Experience integrating third-party SaaS data: CRM, ERP, marketing platforms, customer service systems, logistics, e-commerce, payment systems, or ad platforms • Experience building data lakehouses, data middle platforms, data platforms, or enterprise-level data warehouses • Experience 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 - 100k depends on experiences, open to negotiation 401(k) PTO Paid Holidays Insurance