Data Engineer I, Worldwide Operations Finance, WW Ops Finance S&A, WW Ops Finance S&A

Full Job Description

Are you excited about turning complex financial data into trusted, actionable insights that help Amazon operate more efficiently across the globe? The Worldwide Operations Finance team is looking for a Business Intelligence Engineer I to help build and scale a unified data platform that supports over 100 active finance users across 11 countries. In this role, you will build scalable data pipelines, create AI-powered dashboards, and deliver curated datasets that drive cost-performance tracking, weekly business reviews, and monthly book closures. You will work at the intersection of operations and finance analytics, replacing legacy regional data stores with consolidated, standardized datasets that tracks 500+ operational and cost metrics at daily granularity. This is a great time to join: the team is revamping the legacy datasets , dashboards and processes and is actively migrating to a new AWS-based infrastructure, giving you hands-on experience working on migrating to CI/CD deployed pipelines, while expanding coverage to new markets.

Key job responsibilities

• Design, build, and maintain scalable ETL pipelines and data solutions using Python, SQL, Spark, and AWS services (S3, Glue, Redshift, Airflow, EMR, Lambda)
• Optimize query performance and resource usage across the data platform, participate in code reviews and design discussions, and identify process improvements that increase automation and reduce manual effort
• Document solutions thoroughly to ensure maintainability, validate outputs against source data and business logic, and handle data in accordance with Amazon policies
• Develop AI-powered dashboards and intelligent agents that automate metrics bridging, commentary generation, and deep-dive queries, enabling finance users to make faster data-driven decisions Collaborate with finance stakeholders, data engineers, and product teams to gather requirements, integrate data feeds from multiple sources, and ensure datasets are accurate, accessible, and aligned with evolving business needs

A day in the life You start your morning reviewing pipeline health dashboards and triaging data quality alerts on cost data flowing across supply chain legs. By mid-morning, you are collaborating with finance analysts to reconcile discrepancies between finance, stores, and operations datasets. After lunch, you work with product teams and finance partners to define cost estimation logic for newly launched business lines, warehouse types, and carrier nodes, ensuring the allocation approach is consistent across markets. You then build and test the ETL pipeline to support it. You wrap up the day with a design session on upcoming datasets, ETL pipelines, dashboards or conduct code reviews to make sure that our solution scales across all regions.

About the team Our team within Operations Finance builds and operates the unified data platform that powers financial reporting, bridging, and analytics for over 100 finance customers across all Rest of World (RoW) countries. We support 10+ data producer teams and are focused on scaling automation, improving data quality, and incorporating AI-driven workflows for both our builders and our customers. If you want to shape how a global finance organization interacts with data, this is the place to do it.

Basic Qualifications

• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
• Experience with data visualization software (e.g., AWS QuickSight or Tableau) or open-source project
• Experience with one or more scripting language (e.g., Python, KornShell)
• Require 3+ years of creating production quality data pipelines along and exposure to data modelling

Preferred Qualifications

• Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
• Master's degree

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3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience Experience with data visualization software (e.g., AWS QuickSight or Tableau) or open-source project Experience with one or more scripting language (e.g., Python, KornShell) Require 3+ years of creating production quality data pipelines along and exposure to data modelling