The Data Management Associate supports the transformation of raw and fragmented data into structured, reliable, and analysis-ready information for organizational use. This role focuses on building and maintaining data pipelines, supporting data requests, and improving dashboards to enable timely, accurate insights for management and Executive Leadership Team decision-making.
Working closely with the Data Management Manager and stakeholders, the executive contributes to scalable, secure, and high-quality data systems that strengthen organizational reporting and performance.
1. Data Pipeline Development & Management
• Analyze, design, and build ETL/ELT processes for small to medium-scale projects under guidance from senior team members.
• Maintain and optimize existing data pipelines to improve performance, scalability, and reliability.
• Support the monitoring of data workflows and assist in troubleshooting issues to ensure smooth data operations.
• Document data architecture, workflows, and technical processes for continuity and improvement.
2. Data Analysis & Reporting Support
• Develop SQL queries and perform data analysis to support reporting and operational requirements.
• Support the preparation and enhancement of dashboards and reports for business users and management.
• Translate raw data into structured and usable formats for analysis and decision-making.
• Work with stakeholders to clarify reporting needs and deliver timely data solutions.
3. Data Quality & Optimization
• Perform data profiling, cleansing, and validation to ensure data accuracy and consistency.
• Identify data gaps, inconsistencies, and process bottlenecks, and recommend improvements.
• Support initiatives to improve data structure, standardization, and usability across systems.
• Contribute to continuous improvement of data processes and workflows.
4. Data Governance & Compliance
• Support data governance initiatives and ensure adherence to data management standards and policies.
• Maintain proper documentation of ETL processes, data flows, and reporting logic.
• Ensure data security, confidentiality, and integrity in handling organizational information.
• Assist in implementing best practices in data management and reporting.
5. Collaboration & Stakeholder Support
• Work closely with the Data Management Manager, IT, and business teams to understand data requirements.
• Provide support in resolving data requests and system enhancements.
• Contribute to cross-functional data initiatives that strengthen organizational reporting and decision-making.
• Demonstrate teamwork, accountability, and responsiveness in delivering data solutions.
• Bachelor's degree in Computer Science, Information Systems, Engineering, or related discipline.
• 2–3 years of experience in ETL development, data integration, or data engineering. Fresh graduates with relevant skills are encouraged to apply.
• Working knowledge of data visualization tools (e.g., Power BI or similar platforms).
• Hands-on experience with database queries (MSSQL, including DDL and DML).
• Knowledge of Python or other programming languages is an added advantage.
• Strong analytical and problem-solving skills with attention to detail.
• Fast learner, able to work independently with guidance and contribute effectively as part of a team.
• Good communication skills in English and Chinese (written and spoken).
• Strong interest in Data Analytics and Data Science.
• Analytical Thinking – Interprets data requirements, analyses datasets, and provides structured solutions.
• Technical Acumen – Applies knowledge of data pipelines, databases, and scripting to support data integration and reporting.
• Data Optimization – Improves data accuracy, structure, and usability to support analysis and decision-making.
• Collaboration – Works effectively with stakeholders across departments to deliver data solutions.
• Accountability – Takes ownership of deliverables, timelines, and data quality under guidance of the Data Manager.
• Innovation & Learning – Continuously builds knowledge of new tools, technologies, and data practices.
• Accuracy, timeliness, and user satisfaction in fulfilling data requests and dashboard enhancements.
• Efficiency and effectiveness of scripts, queries, and data pipelines.
• Quality, reliability, and usability of reports and dashboards produced.
• Contribution to organization-wide data transformation and increased use of data for decision-making.
• Compliance with data governance standards and proper documentation practices.
• Continuous process improvement and optimization of data workflows.
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