Data Engineer, Global Procurement Technology
Amazon.com
Amazon's Global Procurement Organization manages indirect supply chain for World Wide Operations and Delivery Services. We work with massive amounts of data and leverage them for advanced applications.
We are Data Engineers to build the facilities to help enable the Global Procurement Organization to gain deep insights into all facets of indirectly supply management and apply advanced data science for automation and decision making.
You are someone who is customer focused, relentless and driven to help us build out a brand-new initiative. You will collaborate closely with product managers, developers and leaders across the org. To be successful in this role, you should have broad skills in database design, be comfortable dealing with large and complex data sets, modeling and designing a strong data warehouse and building self-service data platforms for stakeholders to utilize the data we manage.
Key job responsibilities
* Own and design end-to-end data pipelines and models for complex procurement domains including spend analytics, supplier risk, contract compliance, and catalog management
* Drive technical design reviews and author architecture documents for new data systems and platform components
* Lead scalability and reliability improvements — identify bottlenecks in existing infrastructure, define solutions, and execute with or without a team behind you
* Build frameworks and reusable components that improve engineering productivity and standardize data patterns across GPT data products
* Partner with applied scientists and BIEs to define the data infrastructure needed to power ML models, forecasting engines, and self-service analytics platforms
* Define and enforce data quality standards — including data contracts, freshness SLAs, lineage tracking, and observability instrumentation
* Translate ambiguous business requirements from procurement operations, finance, and leadership into well-scoped technical solutions
* Mentor and develop junior data engineers through code reviews, design feedback, and pair programming
* Operate across multiple concurrent workstreams; manage tradeoffs between speed, correctness, and technical debt
We are Data Engineers to build the facilities to help enable the Global Procurement Organization to gain deep insights into all facets of indirectly supply management and apply advanced data science for automation and decision making.
You are someone who is customer focused, relentless and driven to help us build out a brand-new initiative. You will collaborate closely with product managers, developers and leaders across the org. To be successful in this role, you should have broad skills in database design, be comfortable dealing with large and complex data sets, modeling and designing a strong data warehouse and building self-service data platforms for stakeholders to utilize the data we manage.
Key job responsibilities
* Own and design end-to-end data pipelines and models for complex procurement domains including spend analytics, supplier risk, contract compliance, and catalog management
* Drive technical design reviews and author architecture documents for new data systems and platform components
* Lead scalability and reliability improvements — identify bottlenecks in existing infrastructure, define solutions, and execute with or without a team behind you
* Build frameworks and reusable components that improve engineering productivity and standardize data patterns across GPT data products
* Partner with applied scientists and BIEs to define the data infrastructure needed to power ML models, forecasting engines, and self-service analytics platforms
* Define and enforce data quality standards — including data contracts, freshness SLAs, lineage tracking, and observability instrumentation
* Translate ambiguous business requirements from procurement operations, finance, and leadership into well-scoped technical solutions
* Mentor and develop junior data engineers through code reviews, design feedback, and pair programming
* Operate across multiple concurrent workstreams; manage tradeoffs between speed, correctness, and technical debt
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