I need an experienced Python engineer who works confidently with AWS Glue to build and manage a small suite of data-integration jobs for a Hyderabad-based project. The core of the work is to design and automate Glue ETL pipelines that pull data from our production databases, catalog it accurately, and transform it into analytics-ready tables. Here is what I expect from the engagement: • Develop, test, and deploy Glue ETL jobs in Python. • Populate and maintain the Glue Data Catalog so new tables are discoverable and properly version-tracked. • Implement efficient transformation logic that cleans, enriches, and partitions data for downstream reporting. • Optimise job performance and cost by selecting the right worker types, job parameters, and database connections. Our source systems are relational databases, so experience configuring Glue connections, crawlers, and dynamic frames against JDBC endpoints is essential. Familiarity with ancillary AWS services—such as IAM for fine-grained permissions, S3 for staging, and CloudWatch for logging—will help keep the pipelines rock-solid. Deliverables will be accepted when: 1. All Glue jobs run from start to finish without manual intervention. 2. Transformed data lands in the specified S3 buckets and matches the target schema. 3. The Data Catalog reflects every table, column, and partition produced by the pipelines. 4. Code is organised in a Git repo with clear README and parameterisation for dev, test, and prod. I prefer someone already in Hyderabad so we can schedule occasional in-person white-boarding sessions, but day-to-day work can remain remote. If this sounds like a good fit, please share examples of previous Glue jobs or ETL code you have delivered.