✦ Luna Orbit — Data & Analytics

Data Engineer, Data Platform Management, Grocery Management Tech

at Amazon.com

📍 US, TX, Austin Unknown Posted March 26, 2026
Type Not Specified
Experience mid
Exp. Years Not specified
Education Not specified
Category Data & Analytics

This role involves leading data engineering initiatives to support grocery store operations, focusing on data transformation, integration, and analytics infrastructure.

  • Support data pipelines
  • Transform datasets
  • Collaborate with cross-functional teams
  • Optimize data performance
  • Lead data integration projects

The technical environment includes data pipelines, Python, Java, proprietary frameworks, and cloud-based data infrastructure, emphasizing performance and automation.

The ideal candidate is a mid-level data engineer with experience in data modeling, ETL, and data pipelines, with proficiency in Python and Java. They should be capable of leading data integration and analytics projects within a grocery management context.

Data ModelingETLSQLData PipelinesData Integration
PythonJavaData InfrastructurePerformance OptimizationAutomation
JiraProprietary Amazon Framework
Data ModelingETLSQLData PipelinesData IntegrationPythonJavaData InfrastructurePerformance OptimizationReportingAnalyticsData ManagementData SupportData ArchitectureData Solutions
Data IntegrationReportingAnalyticsData ModelingETLPythonJavaData PipelinesData InfrastructurePerformance Optimization
Organizational SkillsDecisivenessOwnershipCollaborationProblem-solving
Industry Grocery Retail IT
Job Function Manage and develop data pipelines and infrastructure for grocery data analytics
Role Subtype Data Engineer
Tech Domains Amazon Web Services, SQL / PostgreSQL
Data EngineerData ModelingETLData PipelinesData IntegrationPythonJavaData InfrastructurePerformance OptimizationReportingAnalyticsData ManagementData SupportData ArchitectureData Solutionsdata engineeretlsqldata pipelinesdata modelingpythonjavadata infrastructureperformance optimizationdata integration

No experience with data modeling or ETL, Lack of SQL skills, No experience with data pipelines, No familiarity with data infrastructure, Lack of collaboration skills

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