Staff Data Analyst

What You Will Do

• Data Strategy & Governance: Lead high-impact projects to improve the quality and depth of selection data, ensuring "source of truth" reliability for multi-billion dollar business decisions.
• Pipeline & ETL Mastery: Architect and maintain robust data ETL processes to integrate disparate signals from market trends, vendor performance, and customer demand.
• AI & Prompt Engineering: Leverage Large Language Models (LLMs) and advanced prompt engineering to automate catalog categorization, extract insights from unstructured vendor data, and accelerate internal analytical workflows.
• Knowledge Management: Own and evolve the Retail Team Knowledge Base , ensuring that technical documentation, data dictionaries, and business logic are centralized, searchable, and up-to-date for Data Analysis using AI tools.
• Business Empowerment: 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.
• Advanced Decision Modeling: Structure large-scale datasets to build decision models for SKU rationalization, automated replenishment, and competitive benchmarking.
• Optimization & Simulation: Run complex simulations to predict the impact of selection changes on customer retention and long-term profitability.
• Stakeholder Influence: Translate complex analytical findings into clear, actionable narratives for executive stakeholders across Retail and Supply Chain teams.
• Mentorship: Act as a technical beacon for the analytics community, guiding senior and junior analysts in their technical development and problem-solving frameworks.
• Basic Qualifications
• Experience: 8+ years of relevant working experience in a data analytics or business intelligence role.
• Technical Stack: Expert-level 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: Mastery of 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.

Preferred Qualifications

• 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).

Type of work model

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