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Metro location, popular data engineer role, and broad AWS/Spark skillset increase applicant competition.
Core data engineering skills are transferable across industries but AWS and Spark-Scala focus adds moderate domain bias.
Explicit 7–12 years requirement, minimum 4+ years AWS experience, and specific Spark-Scala/Redshift skills raise filtering strictness.
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Design, build, and maintain scalable cloud-native data pipelines and warehouses on AWS using services like Redshift, Kinesis, EMR, and AWS DMS.
Develop and optimize ETL/ELT frameworks and real-time streaming data processing applications using Spark-Scala.
Implement data migration, workflow orchestration, infrastructure as code, monitoring, security controls, and API integrations to ensure scalable, performant, and compliant AWS data workloads.
7+ years overall experience in Data Engineering and Cloud Data Platforms.
Minimum 4+ years hands-on AWS Data Engineering experience with expertise in Spark-Scala development.
Bachelor's degree in Computer Science, IT, Engineering, or related field.
Strong SQL skills and experience building enterprise-grade data lakes and data warehouses.
Experienced working with large-scale AWS native services including Athena, Redshift, Kinesis, CloudFormation, and EMR.
Comfortable operating within Agile/Scrum environments collaborating with architects and cross-functional teams.
Familiar with data governance, security practices, and performance optimization of AWS workloads, plus certification in AWS Data Analytics or Solutions Architect is a plus.