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Business Analyst, Global Logistics Analytics

il y a 3 semaines


Luxembourg Findel Airport Amazon Temps plein

DESCRIPTION

Amazon has focused on being "Earth's most customer-centric company." The Global Logistics team supports and advances this mission by developing game-changing international capabilities and innovative supply chain strategies. We "think globally, act locally" to revolutionize a worldwide customer experience and fulfill our promise to become the world's first truly global e-commerce company. The Global Logistics team is looking for a Business Analyst to support the launch of a strategic network initiative for Global Mile. They will partner closely with central tech, program and product teams to help support daily operational execution of the new and existing initiatives. We invite you to be part of the team that defines how Amazon changes the world of international logistics, one shipment at a time.

Key job responsibilities
- Design, development and maintenance of data pipelines, tables, reports and dashboards.
- Development of moderately to highly complexity data processing jobs using appropriate technologies (e.g. SQL, Python, Spark, AWS Lambda, etc.).
- Collaborating with stakeholders to understand business domains, requirements, and expectations. - Resolve data issues
- Deliver moderately complex data analyses to facilitate business & technical decision making, collaborating with other tech teams (e.g. Data Engineering) as needed.
- Actively manage the turnaround time and deliverables of projects, anticipate risks and resolve issues.
- Adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.

BASIC QUALIFICATIONS
- Experience in tax, finance or a related analytical field
- Experience writing complex Excel VBA macros
- Bachelor's degree or equivalent
- Experience defining requirements and using data and metrics to draw business insights
- Experience with SQL or ETL

PREFERRED QUALIFICATIONS
- Experience working with Tableau
- Experience using very large datasets