Staff Data Analyst - Catalog and PBS Team

• Data Strategy & Insight:
  • Design, develop, and maintain critical data pipelines, metrics, and reporting focused on Catalog and PBS performance, quality, and usage (e.g., data freshness, completeness, classification accuracy, service reliability, API usage).
  • Conduct deep-dive, end-to-end analysis to identify root causes of data quality issues, platform bottlenecks, and areas for strategic improvement within the catalog and PBS ecosystem.
  • Proactively monitor key performance indicators (KPIs) and develop predictive models to forecast future trends and flag potential data or system risks before they impact the business.
• Data Governance & Quality:
  • Establish and enforce data quality standards for the core Catalog data, collaborating with Data Engineering and Data Governance teams.
  • Lead initiatives to audit, cleanse, and validate large-scale datasets, ensuring the data used for business reporting and product features is accurate and trustworthy.
  • Act as a subject matter expert on Catalog and PBS data structures, definitions, and lineage, mentoring junior analysts and technical partners.
• Cross-Functional Partnership:
  • Partner closely with Product Managers, Engineering Leaders, and Business Stakeholders to translate complex business questions and requirements into technical analysis and data models.
  • Deliver clear, compelling, and concise data visualizations (dashboards, reports) and presentations to both technical and non-technical executive audiences to inform product roadmap and business strategy.
  • Drive the experimentation (A/B testing) analysis for new Catalog features or PBS improvements, rigorously measuring their impact on key metrics.

• Experience: 5+ years of experience in a Data Analyst, Business Intelligence, or related quantitative role, preferably supporting Product, Platform, or E-commerce Catalog teams.
• Technical Proficiency: Expert-level SQL skills (writing complex, optimized queries), and strong proficiency in a statistical programming language (Python or R) for data manipulation and statistical analysis.
• Data Visualization: Mastery of data visualization tools (e.g., Tableau, Power BI, Looker) to create impactful dashboards and tell data stories.
• Statistical Foundation: Strong understanding of statistical analysis, experimental design, and quantitative methodologies to draw defensible conclusions.
• Communication: Excellent written and verbal communication skills with the ability to articulate complex data insights and technical constraints to diverse stakeholders.

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