Data Analyst

Retail and Selection is the engine behind Coupang’s massive product catalog, ensuring we offer the right products at the right price with the best availability. As we scale, the complexity of managing millions of SKUs and thousands of vendor relationships grows exponentially.
We are looking for a Data Analyst to join the Retail and Selection team. In this role, you will use your expertise in SQL, data ETL development, and data integrity to ensure that necessary information is available for all levels of analysis at Coupang. Additionally, you will contribute to analytics projects as needed. You will work on projects that define the quality, availability, and depth of data needed to optimize our selection, pricing, and inventory health.

• →Work on high-impact projects to improve the quality and depth of selection data, ensuring "source of truth" reliability for multi-billion dollar business decisions.
• →Maintain robust data ETL processes to integrate disparate signals from market trends, vendor performance, and customer demand.
• →Leverage Large Language Models (LLMs) and advanced prompt engineering to automate catalog categorization, extract insights from unstructured vendor data, and accelerate internal analytical workflows.
• →Support the onboarding of business users, designing training programs and self-service dashboards that enable Category Managers and Vendors to make data-driven decisions independently.
• →Structure large-scale datasets to build decision models for SKU rationalization, automated replenishment, and competitive benchmarking.
• →Run complex simulations to predict the impact of selection changes on customer retention and long-term profitability.

• Experience: 2+ years of relevant working experience in a data analytics or business intelligence role.
• Technical Stack: Proficiency in SQL and Python/R for data extraction and manipulation.
• AI Proficiency: Practical experience with prompt engineering and using generative AI tools to solve data problems or automate content generation.
• Visualization: Experience in data storytelling using PowerBI, Tableau, or similar tools to communicate insights effectively to non-experts.
• Communication: Business-level fluency in English with a proven ability to teach and onboard non-technical business users.
   

• Data Engineering: Hands-on experience with version control (GitHub) and workflow orchestration tools like Airflow.
• Content & Documentation: Experience managing internal wikis (e.g., Confluence, Notion) or building structured knowledge bases for large organizations.
• Domain Expertise: Previous experience in Retail, E-commerce selection management, or Demand Forecasting.
• Quantitative Depth: Strong knowledge of statistics and ML techniques (e.g., elasticity modeling, clustering for customer segmentation).

• Hybrid

Details to consider 

• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws. 
   

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