Found Description
:Develop, test, tune, and maintain ETL and data pipelines using PySpark, Python, SQL, and AWS services Support ingestion and transformation of flat files, relational databases, APIs, data warehouses, and enterprise data sources Collaborate with business analysts, data architects, QA, DevOps, and senior engineers to implement source-to-target mappings and data solutions Implement CDC, incremental load design, idempotent pipeline processing, and data reconciliation patterns for reliable data movement Maintain technical documentation, mapping specifications, data catalog updates, runbooks, automated tests, and release support materials Hands-on experience with PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing Deep knowledge of Redshift performance tuning including distribution keys, sort keys, compression encoding, Spectrum, materialize...
Knowledge, Skills, and Abilities: