✦ Luna Orbit — Data & Analytics

Data Engineer, SPTC

at Amazon.com

📍 US, WA, Seattle Unknown Posted March 27, 2026
Type Not Specified
Experience mid
Exp. Years Not specified
Education Not specified
Category Data & Analytics

Amazon is seeking a data engineer to develop and maintain scalable data pipelines and models that support decision-making and build trust with sellers.

  • Build data pipelines and models
  • Partner with engineering teams
  • Ensure data quality and lineage
  • Automate data processes
  • Support analytics and decision-making

The role involves working with big data technologies, designing data pipelines, modeling multidimensional datasets, and automating data processes to support analytics and operations.

The ideal candidate is a self-motivated data engineer with experience in building scalable data pipelines, data modeling, and working with big data technologies. They should be analytical, creative, and able to partner effectively with business teams to develop reliable data solutions.

Data engineering toolsData modelingETL designData pipelinesBig data technologies
Data lineageData governanceReal-time processingData architectureAutomation tools
ETL toolsData modeling toolsData pipelines
Data engineeringETLData pipelinesData modelingBig data technologiesData sourcesData lineageData integrationAutomationData architecture
Data engineeringETLData modelingBig data technologiesData pipelinesData sourcesData lineageData integrationAutomationScalable data systems
Self-starterProblem-solvingAnalytical thinkingCollaborationCreativityTenacityPartnering effectively
Industry E-commerce
Job Function Developing scalable data solutions for business insights
Role Subtype Data Engineer
Tech Domains Data & Analytics
Data EngineerETLData pipelinesData modelingBig data technologiesData sourcesData lineageData integrationAutomationScalable data systemsData architectureData governanceReal-time processingData platformsAnalyticsdata engineeretldata pipelinesbig datadata modelingautomationdata sourcesdata lineagedata integrationscalable data systems

Lack of experience with data engineering tools, No experience in data modeling or pipelines, Inability to work with big data technologies

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