Data Analyst – Fulfillment

Global-e (Nasdaq: GLBE) is the world’s leading platform for global direct-to-consumer e-commerce, enabling brands and retailers to seamlessly sell worldwide, delivering localized experiences across markets.

Trusted by some of the world’s most recognized brands including Adidas, Alo Yoga, Logitech, Hugo Boss, Marc Jacobs, and Victoria’s Secret, we operate on a massive global scale through dynamic teams across North America, EMEA, and APAC.

Our technology-based end-to-end solution combines advanced localization capabilities, streamlined global operations and extensive big-data analytics, covering everything merchants need to scale worldwide. We’re a fast-moving, highly collaborative company where innovation, creativity, ownership, and impact are part of everyday work.

The Data Analyst will be responsible for analyzing and optimizing the cost and pricing structures of the company’s fulfillment services. This role will require a deep understanding of operational data and cost structures. The ideal candidate will combine strong analytical and technical abilities with a business mindset to support competitive pricing while driving profitability.

Key Responsibilities:

• Support the execution of competitive and profitable pricing strategies through data analysis and modeling.
• Cost Analysis: Conduct detailed cost analysis, identifying cost variables which might impact pricing models.
• Analytical Tools Development: Design, build and maintain analytical tools and models (in Python, SQL and BI platforms) to support pricing, cost analysis and business decision-making.
• Market and Competitor Benchmarking: Analyze market trends, customer data, and competitor pricing to ensure our services remain competitively priced while maintaining profitability.
• Provide data and insights to Sales, Account Management and Operations teams, supporting pricing and business decisions.
• Data-Driven Optimization: Continuously analyze pricing and operational performance, build reports and dashboards, and identify opportunities to optimize revenue and margins through automation and better use of data.