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Design, build, and maintain scalable ETL and data pipelines on AWS using Python/PySpark. Develop Glue/Lambda/Step Function workflows, event-driven integrations, and data lake/warehouse solutions. Tune SQL, implement monitoring/validation, support API/JSON integrations, troubleshoot operations, document and automate deployments, and mentor junior engineers.

Lead analytics strategy and enterprise semantic/reporting layers across QuickSight/Tableau and Redshift/Athena. Partner with data engineering on domain models, enforce data quality/governance, optimize analytics performance/cost, mentor analysts, and communicate insights to executives.

Design and implement scalable batch and near-real-time data pipelines and storage across AWS. Define event-driven integrations, ensure data quality and observability, optimize performance, lead production troubleshooting and migrations, review designs and CI/CD practices, and collaborate with architects and stakeholders to align solutions to 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.