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Metro mid-level AWS/Spark data engineer role with broad skill requirements at a known employer increases competition.
Core AWS, Spark and SQL data engineering skills are easily transferable across industries, so sensitivity is low.
Explicit 3+ years plus many mandatory AWS, Spark, Iceberg and data engineering skills raises filtering strictness.
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Design, develop, and optimize scalable cloud-native AWS data platforms including batch and analytical pipelines using Spark/PySpark and Apache Iceberg.
Ensure reliability, performance, and operational excellence of large-scale data engineering solutions on AWS.
Implement monitoring, alerting, and production support for data pipelines with focus on performance tuning and data quality.
3+ years of hands-on experience building large-scale AWS data engineering solutions.
Strong expertise in SQL (analytical queries, window functions, stored procedures), Spark/PySpark, Python, and Apache Iceberg.
Proficiency with AWS services: EMR, S3, Athena, Glue Catalog, Aurora PostgreSQL, Lambda, CloudWatch, SQS, SNS, EventBridge, IAM.
Work Experience Required: 3+ years in relevant data engineering roles with AWS-based solutions.
Experienced with high-performance batch and streaming data pipelines and Spark/PySpark performance tuning in AWS environments.
Familiar with Lakehouse/Data Lake architectures and data modeling concepts, including Apache Iceberg and Aurora PostgreSQL performance optimization.
Comfortable with implementing CI/CD, monitoring, debugging, and production support for complex data engineering systems, with skills to manage stakeholder communications.