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Mid-level role, metro location, and common AWS/data skills increase applicant competition.
Moderate: cloud data platform skills transferable but require AWS-specific experience.
Explicit 6–8 years plus specific AWS data stack requirements make filtering strict.
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Own the development and management of AWS data pipelines utilizing AWS data stack including S3, EMR/Glue, IAM, Airflow, Redshift, Athena, SageMaker, Terraform, and Github Actions.
Lead data platform engineering activities involving big data frameworks like Spark and Hive with emphasis on data lake architecture and metadata/catalog management.
Participate in or drive migration and modernization projects from on-premises or Cloudera environments to AWS cloud infrastructure, including VPC, subnet, and networking setup.
6-8 years of relevant work experience in AWS data engineering or platform engineering roles.
Hands-on experience with AWS data stack technologies: S3, EMR/Glue, IAM, Airflow, Redshift, Athena, SageMaker, Terraform, Github Actions.
Experience with big data frameworks such as Spark and Hive.
Experience or exposure to VPC, subnet, and network setup; prior involvement in migration or modernization projects (on-premises to AWS or Cloudera to AWS).
Experienced AWS data platform engineer with deep understanding of data lake architectures and metadata/catalog systems.
Proven ability to execute complex data migration or modernization projects involving cloud data infrastructures.
Comfortable managing end-to-end cloud platform components and networking for scalable data solutions at a senior technical level.