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.
2. 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.
3. Manage modern data lake architecture
Work with Apache Iceberg tables.
Perform migrations from traditional Parquet tables.
Support schema evolution, time travel, and ACID transactions.
4.
Production support and troubleshooting
Investigate Glue jobs that suddenly become slow.
Debug Lambda timeouts.
Fix missing records and data quality issues.
Resolve Redshift performance problems.
Perform root cause analysis (RCA).
5. Infrastructure as Code
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.
6. Security
Manage AWS Secrets Manager.
Configure Lambda-based secret rotation.
Ensure Terraform does not overwrite rotated passwords.
Implement IAM least-privilege access.
7. Backup and Disaster Recovery
Work with enterprise backup tools like Rubrik (their setting may use Grax for Salesforce).
Validate backup jobs.
Perform restores.
Support disaster recovery testing.
Verify restored data.
8. Data Quality
Validate source and target record c
📌 AWS DATA ENGINEER (Hyderabad)
🏢 ADV TECHMINDS
📍 Hyderabad
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