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Design and implement scalable batch and near-real-time data pipelines and storage on AWS. Define data architecture, ensure observability, optimize performance, lead production troubleshooting and migrations, enforce CI/CD and deployment best practices, and collaborate with stakeholders and platform teams to align solutions with business needs.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.

Lead design and governance of KPIs, semantic layers, and analytics assets. Perform advanced SQL tuning across RDS/Oracle/Redshift and cost-aware Athena querying. Build reproducible Python analyses, data validation, anomaly detection, deep-dive analytics, source-to-target mapping, and drive BI dashboard UX and version-controlled analytics engineering practices.

Assist in building and maintaining ETL pipelines using Python and PySpark; develop workflows with AWS Glue, Lambda, and Step Functions; work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, and reporting; implement monitoring, logging, and error handling; ingest API/JSON data; collaborate with engineers, analysts, and stakeholders; support code management, documentation, and deployments.