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Remote mid-level Data Engineer role with popular skillset (5+ years) increases applicant density and competition.
Requires specialized AWS data platform skills, but data engineering experience is generally transferable across industries.
Mandatory 5+ years plus specific AWS data stack, Spark, Python, and IaC requirements make shortlisting strict.
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Lead design, development, and modernization of scalable AWS data pipelines and ETL/ELT workflows.
Migrate legacy workloads (Apache Airflow/MWAA and Amazon EMR) to AWS-native services like AWS Glue.
Implement data governance, metadata management, data quality frameworks, and support analytics/reporting solutions including QuickSight and Power BI migration.
5+ years of experience in Data Engineering.
Strong programming skills in Python and SQL with hands-on Apache Spark/PySpark experience.
Extensive experience with AWS data services: Glue, S3, Redshift, Athena, Lambda, Step Functions, EventBridge, IAM.
Experience with ETL/ELT pipeline design, data modeling, data warehousing, Airflow or MWAA, and Infrastructure as Code (Terraform or CloudFormation).
Experienced in modernizing large-scale AWS cloud data platforms and migrating legacy orchestration and data processing workloads.
Skilled in implementing robust data governance, quality, and metadata frameworks supporting business analytics needs.
Familiar with BI tools (QuickSight, Power BI) and operating in cloud consulting or enterprise data engineering environments.