Design, develop, maintain, and optimize scalable data pipelines and integration workflows across CASETiFY’s core business systems and data platforms.
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Build and support data ingestion, transformation, validation, and delivery processes for structured and semi-structured data from multiple source systems.
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Work closely with BI, analytics, and business stakeholders to understand reporting and analytical needs and translate them into reliable data engineering solutions.
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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.
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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.
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Ensure data pipelines and datasets are accurate, complete, timely, and well governed through strong engineering practices, validation controls, monitoring, and reconciliation mechanisms.
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Collaborate with product, engineering, and platform teams to ensure data solutions are scalable, secure, maintainable, and aligned with enterprise architecture and business priorities.
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Support the implementation of data quality standards, metadata management, lineage, documentation, and data governance practices.
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Monitor and troubleshoot pipeline failures, data issues, and performance bottlenecks, and drive timely resolution and continuous improvement.
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Improve engineering efficiency through automation, standardization, reusable frameworks, and best practices in data development and deployment.
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Support the enablement of AI, machine learning, and advanced analytics use cases by preparing high-quality and sustainable data foundations
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Participate in data platform enhancement, architecture discussions, release activities, and cross-functional delivery planning while maintaining clear technical documentation and operational procedures.
Requirements
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Solid hands-on experience in data engineering, ETL / ELT development, and enterprise data integration.
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Good understanding of data warehousing, data modeling, pipeline orchestration, data transformation, and data lifecycle management.
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Practical experience in building and maintaining data pipelines for analytics, reporting, and operational use cases.
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Strong SQL skills and hands-on experience with modern data platforms, cloud data environments, and related engineering tools.
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Experience working with structured and semi-structured data from multiple business systems and platforms.
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Good understanding of data quality controls, reconciliation, validation, monitoring, and troubleshooting practices.
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Experience in supporting BI and analytics use cases through curated datasets, semantic consistency, and well-structured data models.
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Familiarity with version control, automation, deployment processes, and engineering best practices in data environments.
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Good problem-solving and analytical skills with the ability to identify data issues and translate business needs into structured technical solutions.
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Able to work collaboratively with BI, analytics, engineering, product, and business stakeholders in cross-functional environments.
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Known for promoting reliability, data accuracy, structured thinking, and continuous improvement.
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At least 4-6 years of relevant working experience in data engineering, data platform development, or related roles.
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Experience in eCommerce, retail, omnichannel, supply chain, finance, or other data-intensive environments is preferred.
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Familiarity with enabling data foundations for AI, machine learning, or advanced analytics is a plus.
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Experience in multicultural and fast-paced environments is preferred.
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Happy to work in a buzzing multicultural environment, with proficient spoken and written English; Chinese is a plus.