Data Analytics Engineer - Shenzhen

Purpose of Position The Data Analytics Engineer will be responsible for designing, building, and maintaining scalable analytics data solutions that support CASETiFY’s business intelligence, KPI reporting, dashboarding, and insight generation across the organization. The incumbent will work closely with BI, Data Engineering, product, business, and operations teams to ensure analytics data is trusted, structured, and readily available for decision-making. The incumbent is expected to bring solid hands-on experience in analytics engineering, data modeling, metric definition, data transformation, and report development, with practical understanding of how to bridge raw data and business reporting needs. This role will contribute to building strong semantic foundations, consistent KPI logic, scalable datasets, and high-quality reporting outputs using BI tools such as Tableau and Power BI in a fast-paced and dynamic environment. Job Description Design, build, and maintain analytics-ready datasets, transformation logic, and semantic models to support dashboards, KPI reporting, and business analysis, Work closely with BI, Data Engineering, and business stakeholders to understand reporting requirements and translate them into scalable analytics data solutions, Develop and maintain curated data models, reusable metrics, and governed datasets that support management reporting, self-service analytics, and insight generation, Develop and enhance reports, dashboards, and visualization outputs using reporting tools such as Tableau or Power BI to support recurring and ad hoc business reporting, Define and implement transformation logic, business rules, and data mappings to ensure consistency and trust in analytics outputs across functions, Support KPI setup and metric governance by aligning definitions, calculation logic, metadata, and reporting standards across different business domains, Ensure dashboards and reports are supported by reliable, efficient, and well-structured data models, Work with Data Engineering teams to ensure source-to-report data pipelines are sustainable, scalable, and aligned with enterprise data architecture, Establish and maintain strong metadata, documentation, lineage, and semantic consistency for analytics datasets and reporting layers, Monitor and troubleshoot data quality issues, model performance, and reporting inconsistencies, and drive timely resolution and continuous improvement, Improve analytics development efficiency through reusable frameworks, standardized modeling approaches, and best practices in data transformation and version control, Support insight analysis and ad hoc analytical requests by preparing fit-for-purpose data assets and ensuring business logic is properly reflected in reporting outputs, Participate in release planning, testing, validation, and cross-functional coordination for analytics changes to ensure reporting quality and business continuity. Requirements Strong hands-on experience in analytics engineering, data transformation, and data modeling for BI and reporting use cases Good understanding of how to bridge raw data, business logic, and reporting needs through structured and scalable analytics data design Solid SQL skills and practical experience in building curated datasets, semantic layers, and reporting-ready data models Experience in KPI setup, metric logic standardization, metadata management, and reporting consistency practices Good understanding of data warehousing concepts, dimensional modeling, and transformation workflows used in modern analytics environments Experience working closely with BI, Data Engineering, and business stakeholders to translate reporting needs into sustainable data solutions Strong understanding of data quality validation, reconciliation, troubleshooting, and documentation practices in analytics delivery Familiarity with modern data and BI tools, version control, and deployment practices in analytics or reporting environments, with strong hand…