Amazon

Business Intelligence Engineer, Ops Finance Transportation

Job Description

The Amazon transportation controllership team is looking for a Business Intelligence Engineer who is data-driven and detail oriented, to partner in expanding our financial data and reporting systems to support our global transportation network. You should have excellent business and communication skills to be able to work with business owners and tech leaders to gather requirements, build data pipelines and reports/dashboards/automations to meet business needs. You stay abreast of emerging technologies, investigating and implementing where appropriate. This person must be strategic and have strong analytical skills, robust interpersonal and influencing skills, strong organizational and project management skills, and is a self-starter. They will need to roll up their sleeves, work both independently and collaboratively across teams, and have a high level of ownership and accountability.

Key job responsibilities
- Learn, publish, analyze and improve management information dashboards, operational business metrics decks and key performance indicators.
- Improve tools and processes, scale existing solutions, and create new solutions as required based on stakeholder needs.
- Design and automate ETL pipelines for business metrics.
- Partner with stakeholders from business, product and operations teams to define priority of BI projects, and reporting / Program Management for BI projects
- Performing deep-dives to derive actionable insights; Creating models to predict outcomes and optimize business processes
- Building self-service reporting tools and dashboards
- Providing SQL support and delivering trainings (SQL, QuickSight, etc.)
- Research the most appropriate methodologies for a given analytical problem.
- Contribute to key business documents.
- Architect and develop scalable data pipelines
- Develop new data models and end to data pipelines.
- Actively participate in design reviews of related tech products.
- Interface with multiple finance and accounting customers, gathering requirements and delivering complete solutions.
- Analyze and solve problems at their root, stepping back to understand the broader context.


A day in the life
Work with the largest dataset in logistics management in the world to drive business insights, support operations and build analytical solutions for partners which can help drive cost control and decision making. As a Business Intelligence Engineer, you will be working in one of the world's largest and most complex data warehouse environments. You will design, implement and support scalable reporting solutions to integrate with multi heterogeneous data sources, aggregate and retrieve data in a fast and safe mode, curate data that can be used in reporting, analysis, machine learning models, Gen Ai and ad-hoc data requests.

About the team
Trans Controllership in Finance is one of the most challenging and exciting spaces to work for, with new geographies, new modes and new carriers being launched every day and being required to seamlessly integrate with the Transportation technical systems for automatic Ingestion of Freight invoices, thorough auditing, timely payment and for proper accounting to keep our books clean and accurate. - 3+ 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 using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience working directly with business stakeholders to translate between data and business needs
- Experience in discovering business requirements and creating insightful dashboards tailored for various levels of the organization, including senior leadership.
- The successful candidate will be able to deliver products that exceed customer satisfaction, ensure data accuracy and completeness, automate manual tasks and develop efficient data pipelines.


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