Business Intel Engineer, WW Stores IPAT - SWIFT

Amazon; Milano; In loco; EUR 50.000 - 75.000; Tempo pieno

Descrizione del lavoro

A leading global e-commerce company is seeking a Business Intelligence Engineer to provide analytical support and reporting solutions. The role requires over 5 years of experience in data analysis, proficiency with SQL, and familiarity with data visualization tools. Join a dynamic team that values innovation and data-driven decision-making in a fast-paced environment.

Competenze

• 5+ years of experience analyzing and interpreting data.
• Experience with data visualization tools like Tableau or Quicksight.
• Familiarity with data modeling and ETL processes.

Mansioni

• Query large datasets across multiple platforms.
• Design and maintain user-friendly reports and dashboards.
• Analyze historical data for insights and recommendations.

Conoscenze

Analyzing and interpreting data

Data visualization with Tableau

Data modeling and ETL pipelines

Statistical Analysis (R, SAS, Matlab)

SQL proficiency

Scripting in Python

Descrizione del lavoro

Business Intel Engineer, WW Stores IPAT - SWIFT

Stores Worldwide Intelligence & Finance Technology (SWIFT) team is responsible for providing reporting, analytics and automation support to multiple teams across Amazon.

At SWIFT, we are looking for high caliber and tested Business Intelligence Engineer (BIE) to contribute to projects with high exposure to Director and VP level business decision making. In this role you will query large databases, build dashboards, automate manual processes, apply latest statistical/science algorithms to provide decision making support. The solutions built by SWIFT team are leveraged by multiple cross-functional teams to deep dive into their respective businesses. You will apply both business and technical acumen in a fast-paced, ambiguous and innovative environment. Our ideal teammate possesses a natural curiosity, sharp analytical skills and is excited to pioneer on behalf of customers.

If you enjoy dealing with high ambiguity, complexity and broad scope that will be at the epicenter of our initiative, come join us!

Key job responsibilities

• Understanding of Amazon’s data resources, which to use, how, and when
• Query large datasets across multiple platforms
• Design, develop and maintain scaled, automated, user-friendly reports, and dashboards enabling stakeholders to manage the business and make effective decisions.
• Develop and support the analytical technologies that give our customers timely, flexible and structured access to their data.
• Defining, developing and maintaining critical business and operational reports reviewed on a weekly, monthly, quarterly, and annual basis
• Analysis of historical data to extract meaningful insights and learnings from large and complicated data sets identify trends and support decision making, including written and verbal presentation of results and recommendations
• Implement training and documentation solutions that enables stakeholders to get the most out of our self-serve reporting tools.
• Work with PM/BIE/DE managers in understanding the business requirements and implementing solutions to support analytics and reports.

About the team

Stores Worldwide Intelligence & Finance Technology (SWIFT) team is responsible for providing reporting, analytics and automation support to multiple teams across Amazon.

Basic Qualifications

• 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience with data modeling, warehousing and building ETL pipelines
• Experience in Statistical Analysis packages such as R, SAS and Matlab
• Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling

Preferred Qualifications

• Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business
• Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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