Data Monitoring Centre – Data Analyst

Job Responsibilities：

1. Data Monitoring & Anomaly Detection

• Participate in the design and maintenance of key performance indicator (KPI) monitoring systems, including real-time monitoring of critical business and system performance metrics.
• Assist in establishing anomaly detection rules and alert mechanisms to identify data fluctuations and abnormal trends in a timely manner, and support root cause analysis.

2. Data Analysis & Reporting

• Assist in the end-to-end development, enhancement, and access management of BI dashboards and reports using tools such as Tableau, Power BI, FineBI, Quick BI, etc.
• Transform routine daily, weekly, and monthly reports, as well as ad-hoc analysis requests, into self-service and dynamic dashboards with automated data updates, enabling business users to access and analyze data independently while reducing manual data extraction and reporting efforts.
• Conduct in-depth analysis of abnormal events, identify potential root causes, and provide actionable insights and recommendations to support business decision-making.

3. Cross-Department Collaboration & Support

• Work closely with business, operations, finance, technology, and other relevant teams to understand business requirements and translate them into practical data analysis solutions.
• Provide data analysis support and interpretation to business teams based on their data requirements.

Requirements：

1. Education & Academic Background

• Bachelor's degree or above in Statistics, Mathematics, Computer Science, Data Science, Information Technology, Information Management, or a related field is preferred.

2. Technical Skills

• Proficient in Excel for data processing and analysis, including PivotTables, formulas, and other commonly used functions; proficient in PowerPoint for report preparation and presentation.
• Familiar with at least one data visualization tool, such as Power BI or FineBI, with the ability to independently develop and maintain monitoring dashboards.
• Familiar with at least one data analysis tool or programming language, such as Python or SQL.

3. Data Analysis & Logical Thinking

• Solid understanding of data analysis methodologies, including descriptive statistics, trend analysis, comparative analysis, funnel analysis, and other commonly used analytical methods.
• Ability to identify patterns and anomalies in complex datasets and conduct root cause analysis.
• Strong logical thinking and business acumen, with the ability to translate business problems into measurable analytical models.

4. Soft Skills & Competencies

• Strong communication and interpersonal skills, with the ability to clearly communicate data insights and recommendations to both technical and business stakeholders.
• Results-oriented mindset with the ability to deliver high-quality analysis within tight deadlines and manage multiple priorities effectively.
• Strong learning ability and initiative, with the willingness and ability to quickly learn and apply new tools and methodologies in practical work.