- Develop ETL/ELT pipelines using AWS Glue, PySpark, Python, and SQL.
- Ingest data from sources like Salesforce, databases, APIs, and S3.
- Load curated data into Amazon Redshift and data lake storage.
1. Optimize Athena and data lake performance
- Convert JSON/CSV data into Parquet.
- Use Snappy compression.
- Design proper partitioning strategies.
- Resolve split limit and performance issues.
- Optimize Athena query costs.
1. Manage modern data lake architecture
- Work with Apache Iceberg tables.
- Perform migrations from traditional Parquet tables.
- Support schema evolution, time travel, and ACID transactions.
1. Production support and troubleshooting
- Investigate Glue jobs that suddenly become slow.
- Debug Lambda timeouts.
- Fix missing records and data quality issues.
- Build AWS infrastructure using Terraform.
- Create reusable modules.
- Manage Auto Scaling Groups, ALBs, IAM, Lambda, Secrets Manager, S3, Redshift, and DynamoDB.
- Troubleshoot Terraform state and production deployment issues.
1. Security
- Manage AWS Secrets Manager.
- Configure Lambda-based secret rotation.
- Ensure Terraform does not overwrite rotated passwords.
- Implement IAM least-privilege access.
1. Backup and Disaster Recovery
- Work with enterprise backup tools like Rubrik (their workplace may use Grax for Salesforce).
- Validate backup jobs.
- Perform restores.
- Support disaster recovery testing.
- Verify restored data.
1. Data Quality
- Validate source and target record counts.
- Maintain audit/control tables (for example in DynamoDB).
- Check checksums and duplicate records.
- Troubleshoot data discrepancies reported by business users.