Data Analytics Specialist for Loss Prevention

hace 1 semana


Ciudad de México, Ciudad de México Servicios Comerciales Amazon Mexico S. de R.L. de C.V. A tiempo completo
Job Description

We are seeking a highly skilled Data Analytics Specialist to join our Loss Prevention team in Mexico. The successful candidate will be responsible for designing, developing, and managing world-class reporting and analytics that support the Amazon Loss Prevention team.

The ideal candidate will have strong technical competency, excellent communication skills, and the ability to work effectively with cross-functional teams. Key responsibilities include:

  • Technical expert in data mining, scoping, and building web and Excel-based reports and analytics
  • Use of data visualization software to develop dashboards, metric tracking, and trends against department goals
  • Meeting deliverable timeframes set by LP leadership, collaborating with product management and stakeholders
  • Maintaining and reporting potential crises, thinking critically, and problem-solving
  • Responding to tickets and requests for support from internal customers, prioritizing based on demand and impact
  • Tracking and reporting key performance indicators to drive performance improvements
  • Evaluating all programs for compliance with Amazon InfoSec standards
Benefits and Requirements

We offer a competitive salary range of $60,000 - $80,000 per year, depending on experience, along with opportunities for growth and professional development. Essential qualifications include:

  • 1+ years of experience with Excel or Tableau (data manipulation, macros, charts, and pivot tables)
  • 2+ years of complex Excel VBA macro writing experience
  • Bachelor's degree or equivalent
  • Experience defining requirements and using data to draw business insights
  • Proficiency in SQL or ETL
  • English language proficiency (B2+)

PREFERRED QUALIFICATIONS include experience working with Tableau, very large datasets, Fraud Prevention and Risk Management, and Python programming.



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