✦ Luna Orbit — Data & Analytics

Software Development Engineer - Big Data, AWS Elastic MapReduce (EMR), EMR EKS

at Amazon.com

📍 US, CA, East Palo Alto Unknown Posted March 19, 2026
Type Not Specified
Experience mid
Exp. Years Not specified
Education Not specified
Category Data & Analytics

This role involves developing and maintaining big data processing systems on AWS, focusing on scalable, distributed data analytics solutions using Hadoop, Spark, and related technologies.

  • Designing big data architectures
  • Building scalable data pipelines
  • Contributing to open source projects
  • Developing real-time processing features
  • Collaborating with open source community

The environment includes Hadoop, Spark, Hive, Impala, HBase, and open source big data tools, with a focus on distributed computing and data processing at scale.

The ideal candidate is a mid-level data engineer with experience in big data technologies such as Hadoop, Spark, and Hive, with a strong understanding of distributed systems and open source contributions. They should have excellent programming skills and a passion for scalable data solutions.

Big DataHadoopSparkHiveDistributed systemsProgramming skills
Open source contributionsCluster managementReal-time processingHBaseHivePigImpala
HadoopHivePigImpalaSparkHBasePrestoOpen source projects
Big DataHadoopSparkHiveDistributed systemsCluster managementOpen sourceProgramming skills
Big DataHadoopHivePigImpalaSparkPrestoHBaseDistributed systemsCluster managementOpen source contributionsProgramming skills
LeadershipCustomer obsessionProblem-solvingDelivery focusCollaboration
Industry Technology / Cloud Computing
Job Function Developing scalable big data analytics solutions on AWS
Role Subtype Data Engineer
Tech Domains Active Directory, Microsoft 365, Amazon Web Services, Hadoop, Spark, Hive, Open source
Big DataHadoopHivePigImpalaSparkHBaseDistributed systemsCluster managementOpen sourceProgrammingAWSAmazon Web ServicesData processingData analyticsOpen source contributionsProgramming skills

Lack of experience with Hadoop or Spark, No programming skills in relevant languages, No experience with distributed systems, No open source contributions

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