• Build and maintain scalable data pipelines in AWS to support ingestion, transformation, and enrichment of structured and semi-structured data • Design and implement Delta Lake tables optimized for ACID compliance, partition pruning, schema enforcement, and query performance across large datasets • Develop ETL and ELT workflows that integrate multiple source systems into a centralized, query-optimized data warehouse architecture • Leverage AWS tools to implement business rules, dimensional joins, and aggregation logic aligned to warehouse modeling best practices • Collaborate with data architects and engineers to implement cloud-native data solutions on AWS using S3, Glue, RDS, and IAM for secure, scalable storage and access control • Optimize pipeline performance through intelligent partitioning, caching, broadcast joins, and adaptive query tuning • Deploy and version data engineering assets using Git-integrated development workflows and automate deployment with CI/CD tools such as GitLab or Jenkins • Monitor pipeline health, job execution, and cluster utilization using AWS CloudWatch, identifying bottlenecks and optimizing cost-performance tradeoffs • Conduct technical discovery and mapping of legacy source systems, identifying required transformations and designing end-to-end data flows • Implement governance practices including metadata tagging, data quality validation, audit logging, and lineage tracking using platform-native features and custom logic • Support ad hoc data access requests, develop reusable data assets, and maintain shared notebooks that meet operational reporting and analytics needs across teams