Senior Associate - Warehouse Data Analytics, Scommerce

Shopee; Klang City; On-site; MYR 60,000 - 90,000; Full time

Job summary

A leading e-commerce platform in Klang City seeks a Business Intelligence Specialist to enhance operational efficiency through data analytics. You will analyze warehouse data, provide actionable insights, and collaborate with teams on data-driven strategies. The ideal candidate has a Bachelor's degree in a quantitative field and at least 3 years of experience in data analytics, proficiency in SQL and Excel, with Python knowledge as a plus. Join us to make a significant impact on our operations.

Qualifications

• Strong proficiency in SQL and Excel for data extraction and reporting.
• Knowledge of Python is an added advantage.
• Minimum 3 years of experience in data analytics.

Responsibilities

• Establish processes to analyze warehouse operational data.
• Support operations by translating insights into strategies.
• Define metrics and build frameworks to track performance.
• Identify risks and inefficiencies through data analysis.
• Write and optimize complex SQL queries.
• Automate reporting pipelines to improve data accessibility.
• Support warehouse expansion initiatives through data modeling.
• Collaborate with stakeholders to ensure data accuracy.

Skills

SQL

Excel

Python

Data analysis

Problem-solving

Education

Bachelor’s degree in Data Analytics, Statistics, Computer Science, Engineering

Job description

The Business Intelligence and Data Analytics team plays a critical role in conducting close-loop data-driven business iterations. As business intelligence specialists and data analysts, our scope of work is not limited to just performance monitoring and reporting, but also in proactively finding actionable insights to help drive high-impact business changes. Our end-to-end data solution reduces the gap between the business teams and technical teams, and achieves real ‘intelligence’ in the iteration cycle. Browse our Business Intelligence and Data Analytics team openings to see how you can make an impact with us.

Job Description

• Establish robust processes to collect, clean, and analyze warehouse operational data (inbound, inventory, outbound) to generate actionable insights.
• Support warehouse operations, product, and logistics teams by translating data insights into operational strategies and execution plans.
• Define key metrics and build monitoring frameworks to track warehouse performance, efficiency, and SLA adherence.
• Proactively identify operational risks, bottlenecks, and inefficiencies by analyzing trends, patterns, and system behaviors, and provide forward‑looking recommendations.
• Write, optimize, and maintain complex SQL queries to support recurring and ad‑hoc data analysis needs across large datasets.
• Automate reporting pipelines and dashboards to improve data accessibility, reduce manual effort, and enable faster decision‑making.
• Support warehouse expansion, automation, and capacity planning initiatives through data modeling, forecasting, and scenario analysis.
• Collaborate closely with cross‑functional stakeholders to ensure data accuracy, alignment with business objectives, and successful project delivery.

Requirements

• Bachelor’s degree in Data Analytics, Statistics, Computer Science, Engineering, or other quantitative/analytical fields.
• Strong proficiency in SQL and Excel for data extraction, analysis, and reporting.
• Knowledge of Python for data analysis or automation is an added advantage.
• Minimum 3 years of relevant working experience in data analytics, preferably in operations, logistics, supply chain, or warehouse environments.
• Proven experience in automating data workflows, reports, or dashboards.
• Strong analytical and problem‑solving skills with the ability to think systematically and understand complex, end‑to‑end operational processes.
• Ability to communicate and present complex insights and recommendations in a clear, structured, and actionable manner.
• Comfortable managing multiple concurrent requests and working independently in a fast‑paced environment.