Data Engineer I , WW Ops Finance S&A
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Description
Do you love turning messy, large-scale data into pipelines people can trust? If you want to solve hard data warehousing problems, partner directly with stakeholders, and see your work impact millions of customers in weeks — not years — we'd like to talk to you. At Amazon, we hire the best minds in technology to innovate on behalf of our customers. Customer obsession is part of our DNA — it's why we're one of the world's most beloved brands. As a Data Engineer, you'll use the latest technology to solve complex problems and see the impact of your work first-hand.
As a Data Engineer in WWSNA, you'll work in one of the world's largest and most complex data warehouse environments, building the infrastructure that powers Amazon's Inbound & Outbound Transportation business. You'll design and operate scalable data models and pipelines — integrating diverse data sources and curating datasets that fuel reporting, analytics, ML models, and ad-hoc requests. This role is highly collaborative: you'll partner with business owners, product teams, and tech leaders to gather requirements and build the data solutions that meet real business needs. Strong business and communication skills matter as much here as technical depth.
Key job responsibilities
- Design, build, and maintain complex data solutions and ETL pipelines using Python, Spark, SQL, and AWS services (S3, Glue, Redshift, MWAA, EMR, Lambda).
- Architect scalable data pipelines that power customer reporting and analytics infrastructure for internal Finance customers.
- Integrate data across heterogeneous sources: extracting, transforming, and loading with a focus on data quality and sound design trade-offs.
- Partner with finance managers, developers, and BI Engineers to deliver data architecture projects and next-generation financial solutions.
- Drive engineering excellence through code reviews, design discussions, and team planning; continually improving reporting processes and expanding self-service automation.
- Identify and implement process improvements that scale existing solutions, launch new ones, and drive innovation based on stakeholder needs.
- Own operational reliability: diagnosing and resolving issues through rigorous root cause analysis to maintain high system availability.
A day in the life
You start your morning reviewing pipeline health dashboards and triaging data quality alerts on cost data flowing across transportation segments. Mid-morning, you collaborate with finance analysts to validate cost allocation logic for a carrier segment being onboarded into the platform. After lunch you build and test an ETL module that integrates a partner team's operational data feed, then pair with a teammate on a code review to ensure the solution scales across regions. Your afternoon wraps up with a design session on extending item-level cost granularity to support new reporting dimensions.
About the team
Our team within Operations Finance builds and operates the self-serve data platform that powers financial reporting, bridging, and analytics for over 2,500 finance customers across multiple regions. 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 customers. If you want to shape how a global finance organization interacts with data, this is the place to do it.
As a Data Engineer in WWSNA, you'll work in one of the world's largest and most complex data warehouse environments, building the infrastructure that powers Amazon's Inbound & Outbound Transportation business. You'll design and operate scalable data models and pipelines — integrating diverse data sources and curating datasets that fuel reporting, analytics, ML models, and ad-hoc requests. This role is highly collaborative: you'll partner with business owners, product teams, and tech leaders to gather requirements and build the data solutions that meet real business needs. Strong business and communication skills matter as much here as technical depth.
Key job responsibilities
- Design, build, and maintain complex data solutions and ETL pipelines using Python, Spark, SQL, and AWS services (S3, Glue, Redshift, MWAA, EMR, Lambda).
- Architect scalable data pipelines that power customer reporting and analytics infrastructure for internal Finance customers.
- Integrate data across heterogeneous sources: extracting, transforming, and loading with a focus on data quality and sound design trade-offs.
- Partner with finance managers, developers, and BI Engineers to deliver data architecture projects and next-generation financial solutions.
- Drive engineering excellence through code reviews, design discussions, and team planning; continually improving reporting processes and expanding self-service automation.
- Identify and implement process improvements that scale existing solutions, launch new ones, and drive innovation based on stakeholder needs.
- Own operational reliability: diagnosing and resolving issues through rigorous root cause analysis to maintain high system availability.
A day in the life
You start your morning reviewing pipeline health dashboards and triaging data quality alerts on cost data flowing across transportation segments. Mid-morning, you collaborate with finance analysts to validate cost allocation logic for a carrier segment being onboarded into the platform. After lunch you build and test an ETL module that integrates a partner team's operational data feed, then pair with a teammate on a code review to ensure the solution scales across regions. Your afternoon wraps up with a design session on extending item-level cost granularity to support new reporting dimensions.
About the team
Our team within Operations Finance builds and operates the self-serve data platform that powers financial reporting, bridging, and analytics for over 2,500 finance customers across multiple regions. 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 customers. If you want to shape how a global finance organization interacts with data, this is the place to do it.
