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Mid-level, popular Data Engineer role at a known AI/data firm with broad AWS/Spark skillset.
Core data engineering skills transfer across industries, but Lakehouse/Iceberg and telecom preference add specificity.
Multiple mandatory technical requirements (Spark, Iceberg, AWS services, SQL) make screening strict.
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Design, develop, and optimize scalable cloud-native data platforms on AWS focusing on high-performance batch and analytical data pipelines.
Build and maintain data pipelines using Spark/PySpark, Apache Iceberg, and various AWS services ensuring reliability, performance, and operational excellence.
Implement monitoring, logging, alerting, and observability solutions to support production data pipelines and ensure data quality and troubleshooting capabilities.
3+ years of hands-on experience building large-scale AWS data engineering solutions.
Strong hands-on expertise in SQL (analytical queries, window functions, stored procedures), Spark/PySpark, and Python.
Experience designing and implementing Lakehouse solutions using Apache Iceberg.
Proficient with AWS services including EMR, S3, Athena, Glue Catalog, Aurora PostgreSQL, Lambda, CloudWatch, SQS, SNS, EventBridge, and IAM.
Experienced in performance tuning and optimization on Spark/PySpark, Apache Iceberg, and Aurora PostgreSQL for scalable data solutions.
Skilled in data modeling, partitioning, file formats, and Lakehouse/Data Lake architectures within cloud-native environments.
Familiar with CI/CD, Git workflows, production support, and preferably holds AWS Data Engineer Associate or equivalent certification.