Data Engineer, Bilingual Mandarin

CWILL

Data Engineer building data infrastructure for Shopify brands. Ingesting data and ensuring data quality while collaborating with domestic teams on various engineering projects.

Posted 7/27/2026full-timeLos Angeles • California, North Carolina • 🇺🇸 United StatesJunior💰 $75,000 - $100,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Proficient in Data Engineering, Data Warehousing, and Data Pipeline Development, with strong SQL and programming skills in Java or Python. Demonstrates a solid understanding of Data Quality, Governance, and collaboration with cross-functional teams.

Highest-signal resume keywords

Data IngestionData Pipeline DevelopmentSQL ProficiencyData Quality ManagementData Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData WarehousingData ModelingSQLJavaPythonData Quality RulesData ValidationData Pipeline OperationsData Governance

Soft Skills

Strong CommunicationCollaborationProblem-SolvingExecution SkillsLearning Ability

Tools & Technologies

SeaTunnelKafkaFlinkSparkDorisDolphinSchedulerStreamParkDataHubGitOLAP Engines

Industry Keywords

Data PlatformsSaaS ProductsData Quality DimensionsMetadata ManagementData LineageData Asset CatalogCross-Border TeamsCloud PlatformsData LakehousesEnterprise-Level Data Warehouses

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureBigQueryCloudDockerERPGoogle Cloud PlatformJavaKafkaKubernetesPostgresPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Ingest data from internal and external business systems, third-party platforms, SaaS products, and external data sources; 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
Participate in layered data warehouse development across ODS, DWD, DWS, and ADS layers; 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
Build and maintain data quality rules for core data pipelines; 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
Participate in building data platform capabilities including data development, task scheduling, monitoring, quality management, governance, and service delivery modules
Use tools such as DolphinScheduler and StreamPark for task management, scheduling orchestration, and real-time task operations
Support the data service layer by delivering standardized APIs, metric services, and data capabilities to internal systems, analytics applications, and business tools
Support underlying data for tools like Superset; ensure data availability for BI dashboards, metric boards, and business monitoring
Participate in designing and implementing data development automation tools and engineering agents
Explore AI agent applications in data development, governance, quality detection, task operations, anomaly diagnosis, and documentation generation
Leverage large language models and automation tools to improve data engineering efficiency, task stability, and platform intelligence

Requirements

What you’ll need

**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
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 & Platform Tools**
Familiar with one or more of: SeaTunnel, Kafka, Flink, Spark (data integration, real-time, or offline processing)
Familiar with one or more of: Doris, ClickHouse, Snowflake, BigQuery, Redshift, Databricks, PostgreSQL (data warehouse, OLAP, or lakehouse systems)
Familiar with one or more of: 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
Candidates are not expected to master all tools, but must have a solid data engineering foundation and the ability to quickly learn new tech stacks
**Data Quality & Governance Mindset**
Understands data quality dimensions: accuracy, completeness, consistency, uniqueness, timeliness, and anomaly detection
Proactively designs data validation rules and can identify and locate data anomalies
Familiar with metadata management, data lineage, data asset catalogs, and data standards; experience with DataHub or similar platforms is a plus
**Collaboration & Communication**
Able to communicate data requirements with analysts, business stakeholders, backend engineers, and product managers
Clearly describes problems, solutions, risks, progress, and deliverables
Comfortable with cross-timezone 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

Comp & perks

Starting Pay: 75 - 100k depends on experiences, open to negotiation
401(k)
PTO
Paid Holidays
Insurance