Kerja Internship Data Analyst Intern (E-commerce), Gaji tinggi MYR 1,000 di Wonderliv Selangor - Maukerja

Requirements

• Studying or graduated in Data Analytics, Data Science, Statistics, Data Engineering, Computer Science, Business, or related field — but open to other degrees if you have a keen interest in IT/AI and have built vibe-coded projects (using tools like Claude, Codex, ChatGPT, etc.)
• Sound analytical thinking — able to interpret data, spot patterns, and draw logical conclusions
• Some experience with BigQuery and Power BI
• Comfortable using Claude and Codex for analysis, reporting, and light coding tasks
• Comfortable communicating with non-technical stakeholders to gather requirements and confirm data accuracy
• Detail-oriented, comfortable with recurring monthly deadlines

Nice to Have

• Exposure to Python (pandas) for data manipulation
• Understanding of e-commerce metrics (AOV, CAC, retention, etc.)
• Basic SQL beyond BigQuery basics (joins, CTEs, window functions)
• Familiarity with data pipelines, working with API calls, and MCP (Model Context Protocol)

What We're Looking For

• Curious — willing to ask “why” behind the numbers, not just pull them
• Can work independently once shown a reporting template
• Clear communicator — can explain numbers to stakeholders
• Comfortable coordinating across teams, not just working solo with data
• Willing and eager to learn new things — genuine interest in growing technical skills matters more than having them all upfront

Core Responsibilities

• Prepare and maintain monthly sales/performance reports across all e-commerce platforms (Shopify, Lazada, Shopee, and any others in scope)
• Build and maintain dashboards (Google Sheets, Power BI, or similar)
• Pull and structure data from BigQuery / Google Cloud data warehouse
• Track key metrics: revenue, conversion, inventory levels, campaign performance
• Gather reporting requirements from stakeholders and translate them into report/dashboard specs
• Liaise with multiple stakeholders (e.g. marketing, ops, management) to verify and reconcile numbers before finalizing reports
• Use AI tools (Claude, Codex) to speed up reporting — summarizing trends, drafting insights, automating repetitive data cleaning and scripting
• Flag anomalies or data quality issues before they hit final reports
• Exposure to adjacent work as needed: light data engineering (pipeline/ETL support), QA testing of internal tools, and basic software engineering tasks