Data Engineer - Shenzhen

Shenzhenmid

Data EngineerData

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Quick Summary

Overview

Job Description Design, develop, maintain, and optimize scalable data pipelines and integration workflows across CASETiFY’s core business systems and data platforms.

Requirements Summary

Solid hands-on experience in data engineering, ETL / ELT development, and enterprise data integration. Good understanding of data warehousing, data modeling, pipeline orchestration, data transformation, and data lifecycle management.

Technical Tools

sqldatabase-designdocumentationecommerceetlmachine-learning

Design, develop, maintain, and optimize scalable data pipelines and integration workflows across CASETiFY’s core business systems and data platforms.
Build and support data ingestion, transformation, validation, and delivery processes for structured and semi-structured data from multiple source systems.
Work closely with BI, analytics, and business stakeholders to understand reporting and analytical needs and translate them into reliable data engineering solutions.
Develop and maintain curated datasets, data models, data marts, and reusable data assets to support business intelligence, operational reporting, self-service analytics, and management dashboards.
Support data integration across key systems such as eCommerce platforms, ERP, OMS, WMS, CRM, marketing systems, finance systems, customer operations platforms, and other enterprise applications.
Ensure data pipelines and datasets are accurate, complete, timely, and well governed through strong engineering practices, validation controls, monitoring, and reconciliation mechanisms.
Collaborate with product, engineering, and platform teams to ensure data solutions are scalable, secure, maintainable, and aligned with enterprise architecture and business priorities.
Support the implementation of data quality standards, metadata management, lineage, documentation, and data governance practices.
Monitor and troubleshoot pipeline failures, data issues, and performance bottlenecks, and drive timely resolution and continuous improvement.
Improve engineering efficiency through automation, standardization, reusable frameworks, and best practices in data development and deployment.
Support the enablement of AI, machine learning, and advanced analytics use cases by preparing high-quality and sustainable data foundations
Participate in data platform enhancement, architecture discussions, release activities, and cross-functional delivery planning while maintaining clear technical documentation and operational procedures.

Requirements

~2 min read

Solid hands-on experience in data engineering, ETL / ELT development, and enterprise data integration.
Good understanding of data warehousing, data modeling, pipeline orchestration, data transformation, and data lifecycle management.
Practical experience in building and maintaining data pipelines for analytics, reporting, and operational use cases.
Strong SQL skills and hands-on experience with modern data platforms, cloud data environments, and related engineering tools.
Experience working with structured and semi-structured data from multiple business systems and platforms.
Good understanding of data quality controls, reconciliation, validation, monitoring, and troubleshooting practices.
Experience in supporting BI and analytics use cases through curated datasets, semantic consistency, and well-structured data models.
Familiarity with version control, automation, deployment processes, and engineering best practices in data environments.
Good problem-solving and analytical skills with the ability to identify data issues and translate business needs into structured technical solutions.
Able to work collaboratively with BI, analytics, engineering, product, and business stakeholders in cross-functional environments.
Known for promoting reliability, data accuracy, structured thinking, and continuous improvement.
At least 4-6 years of relevant working experience in data engineering, data platform development, or related roles.
Experience in eCommerce, retail, omnichannel, supply chain, finance, or other data-intensive environments is preferred.
Familiarity with enabling data foundations for AI, machine learning, or advanced analytics is a plus.
Experience in multicultural and fast-paced environments is preferred.
Happy to work in a buzzing multicultural environment, with proficient spoken and written English; Chinese is a plus.