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Mid-level generalist Data Engineer in a metro with broad AWS/Spark requirements increases candidate competition.
Core data engineering skills are transferable, though financial domain knowledge is moderately preferred.
Explicit 5+ years and required AWS, Spark, Python make screening relatively strict.
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Build, deploy, and maintain scalable, high-performance AWS data pipelines using batch and event-driven architectures.
Develop data processing solutions with Python, AWS Glue, Apache Spark, and containerized environments (Docker, Kubernetes).
Ensure data quality, lineage, orchestration, and operational performance across data ingestion platforms.
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Minimum 5 years experience in data engineering or distributed systems development.
Strong hands-on expertise with AWS services including Glue, Lambda, Step Functions, EventBridge, S3, Athena, and RDS Aurora PostgreSQL.
Advanced proficiency in Python and hands-on experience with Apache Spark and building event-driven data pipelines.
Experienced in designing and operating distributed, event-driven data processing architectures on AWS.
Skilled in ensuring data reliability and governance including data quality, lineage, and orchestration.
Capable of developing RESTful and event-driven APIs and managing containerized workloads within cloud-native and DevOps frameworks.