CASETiFY
DataEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer at CASETiFY. Skills: data engineering, ETL / ELT development, enterprise data integration, data warehousing, data modeling, pipeline orchestration, data transformation, data lifecycle management, SQL, modern data platforms, cloud data environments. Design, develop, maintain, and optimize scalable data pipelines and integration workflows. Build and support data ingestion, transformation, validation, and delivery processes”
What You'll Achieve.
reliable data engineering solutions; support business intelligence, operational reporting, self-service analytics, and management dashboards; data solutions are scalable, secure, maintainable, and aligned with enterprise architecture and business priorities; high-quality and sustainable data foundations
Industry & Context.
problem-solving and analytical skills; identify data issues; translate business needs into structured technical solutions
What They're Looking For.
Must Have
data engineering, ETL / ELT development, enterprise data integration, data warehousing, data modeling, pipeline orchestration, data transformation, data lifecycle management, building and maintaining data pipelines for analytics, reporting, and operational use cases, SQL skills, modern data platforms, cloud data environments, related engineering tools, working with structured and semi-structured data from multiple business systems and platforms, data quality controls, reconciliation, validation, monitoring, troubleshooting practices, supporting BI and analytics use cases through curated datasets, semantic consistency, and well-structured data models, version control, automation, deployment processes, engineering best practices in data environments, problem-solving and analytical skills, identify data issues, translate business needs into structured technical solutions, work collaboratively with BI, analytics, engineering, product, and business stakeholders in cross-functional environments, promoting reliability, data accuracy, structured thinking, and continuous improvement
Nice to Have
eCommerce, retail, omnichannel, supply chain, finance, data-intensive environments, enabling data foundations for AI, machine learning, or advanced analytics, multicultural and fast-paced environments, proficient spoken and written Chinese
What You'll Do.
and optimize scalable data pipelines and integration workflows
Build and support data ingestion
and delivery processes
Develop and maintain curated datasets
and reusable data assets
Support data integration across key systems
Ensure data pipelines and datasets are accurate
Monitor and troubleshoot pipeline failures
and performance bottlenecks
Improve engineering efficiency through automation
Support the enablement of AI
and advanced analytics use cases
Participate in data platform enhancement
architecture discussions
and cross-functional delivery planning
How You'll Work.
Team & Collaboration
Work closely with BI, analytics, and business stakeholders; Collaborate with product, engineering, and platform teams; Able to work collaboratively with BI, analytics, engineering, product, and business stakeholders in cross-functional environments
Communication Scope
spoken and written Chinese
Process & Methodology
cross-functional delivery planning
Full Job Description
Job Description 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 pro
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