Business Intel Engineer II, LATAM 3P BI

Are you ready to make a significant impact in the world of e-commerce? Amazon's LATAM Marketplace team is seeking a talented Business Intelligence Engineer to join our innovative and fast-paced environment. As part of Amazon's commitment to being Earth's most customer-centric company, we connect over 2 million sellers across 10 countries with customers worldwide, offering an unparalleled selection of products at competitive prices.

In this role, you'll be at the forefront of designing and implementing BI solutions for Amazon Marketplace. Your expertise will be crucial in transforming complex data into actionable insights, driving data-driven decision making across the organization. You'll develop and execute BI roadmaps, collaborating with stakeholders from various teams to ensure our marketplace continues to grow and succeed in the LATAM region.

We're looking for a self-motivated professional who thrives in ambiguous environments and possesses strong analytical and problem-solving abilities. The ideal candidate will have experience handling complex data projects, the ability to prioritize and deliver in a fast-paced setting, and excellent business acumen and stakeholder management skills. Your work will directly impact our ability to make informed decisions and drive the success of our business.

Join us in shaping the future of e-commerce and take your career to new heights with Amazon's LATAM Marketplace team. Your contributions will be instrumental in our ongoing efforts to provide the best possible experience for both sellers and customers in this dynamic and growing market.

Key job responsibilities
Data Analysis & Management

Master Amazon's diverse data resources and apply them effectively for business solutions
Write and optimize SQL queries to extract and analyze complex datasets
Develop and maintain ETL processes for reporting solutions
Ensure data quality, consistency, and accuracy across local and global platforms

Business Intelligence Development
Design and implement comprehensive reporting systems and success metrics
Create self-service reporting platforms to enable data-driven decision making
Automate data analysis processes to improve efficiency and scalability
Build and maintain dashboards that provide actionable insights

Collaboration & Strategy
Partner with business teams to understand their challenges and data needs
Transform business requirements into effective analytical solutions
Coordinate with global teams to maintain data infrastructure integrity
Provide data-driven recommendations to support business decisions

Technical Infrastructure
Manage local data infrastructure and reporting systems
Implement best practices for data storage, retrieval, and analysis
Ensure system performance and reliability
Collaborate with global teams on infrastructure improvements

About the team
LATAM Marketplace BI team is responsible for ensuring our stakeholders and leaders have the data they need to make data-driven decisions and drive the success of our business.
We are structured into verticals (business topics) and promote knowledge sharing in between those topics every week.
In our team, a Business Intelligence Engineer can be responsible for 1 or more business verticals, driving his BI projects end to end, this involves: setting up the BI roadmap, managing stakeholders, diving deep on data for that topic and designing and building SQL Tables, KPIs, reports, dashboards and analysis.

- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- 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

- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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