Junior Data Analyst

Primary Job:

Responsible for analyzing and monitoring operational data to support Quality Assurance (QA) activities, developing dashboards and performance reports, and implementing data automation to improve operational efficiency. Identify data anomalies, analyze root causes, and provide data-driven insights to support KPI management, performance evaluation, and continuous process improvement within Customer Service operations.

Job Description:

1. QA Data Analysis & Monitoring

• Responsible for organizing, analyzing, visualizing, and providing day-to-day operational data support for quality assurance activities.
• Build and maintain operational dashboards and reports, primarily using Google Sheets and Looker Studio .
• Develop data monitoring and anomaly alert mechanisms based on business requirements to identify abnormalities in customer service operations in a timely manner.
• Analyze abnormal data, identify root causes, and support relevant teams in implementing improvements.
• Provide ad-hoc data analysis based on management and business team requirements.

2. Data Automation & Process Optimization

• Use Google Apps Script (GAS) and Google Sheets to automate customer service operational processes and improve the team's data processing efficiency.
• Use Python for data cleaning, analysis, and automation.
• Automate repetitive tasks such as data consolidation, report generation, KPI calculations, data updates, and anomaly alerts.

3. QA KPI & Performance Data

• Participate in the development and optimization of KPI and performance data frameworks for QA team members and the overall QA team.
• Establish appropriate performance data analysis and management mechanisms based on different roles and responsibilities.
• Identify performance trends and anomalies at both team and individual levels through data analysis, providing data support for performance management, training, and workforce planning.
• Continuously optimize the methodology and presentation of performance data.

Job Qualification:

1. Experience

• 1–2 years of experience in business data analysis, data engineering, data development, or a related field.
• Experience with process automation is preferred. A strong interest in process automation is highly desirable.

2. Technical Skills

SQL

• Proficient in SQL , with the ability to independently perform data extraction, multi-table joins, and data analysis.
• Familiarity with JOIN, CTE, Window Functions , and other commonly used SQL techniques.

Python

• Familiarity with Pandas, NumPy , and other commonly used Python data analysis libraries.
• Ability to use Python for data cleaning, analysis, and automation tasks.

Google Sheets

• Proficient in Google Sheets , with strong data processing capabilities.
• Familiarity with commonly used formulas, Pivot Tables , data cleaning, and complex data processing.

Google Apps Script

• Basic knowledge of Google Apps Script (GAS) .
• Familiarity with JavaScript fundamentals .

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