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Popular mid-level data engineer role, metro location and broad AWS/Spark skills create high competition.
Data engineering skills are transferable, but AWS/Iceberg specialization and telecom preference increase domain specificity.
Explicit 3+ years plus many mandatory AWS, Spark and Iceberg skills make filters highly strict.
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Design, develop, and optimize scalable cloud-native data platforms on AWS.
Build high-performance batch and analytical data pipelines using Spark/PySpark, Apache Iceberg, and various AWS services.
Ensure reliability, performance, operational excellence, and implement monitoring, logging, alerting for production data pipelines.
3+ years of hands-on experience building large-scale data engineering solutions on AWS.
Strong expertise in SQL (analytical queries, window functions, stored procedures), Spark/PySpark, Python.
Experience with AWS services including EMR, S3, Athena, Glue Catalog, Aurora PostgreSQL, Lambda, CloudWatch, SQS, SNS, EventBridge, and IAM.
Experience designing and implementing Lakehouse solutions using Apache Iceberg.
Experienced in Spark/PySpark performance tuning and optimizing Apache Iceberg and Aurora PostgreSQL for scalability.
Skilled in data modeling, partitioning, file formats, and Lakehouse/Data Lake architectures with emphasis on operational reliability.
Familiarity with CI/CD, Git, data engineering best practices and preferably holds AWS Data Engineer Associate or equivalent certification.