AWS Data Engineer

- Design, develop, and maintain ETL/ELT pipelines using AWS-native services (Glue, Lambda, EMR, Step Functions) - Implement data ingestion from telecom systems like OSS/BSS, CDRs, mediation systems, CRM, billing, network logs - Optimize ETL workflows for large-scale telecom datasets (high volume, high velocity) - Build and manage scalable Amazon Redshift clusters for reporting and analytics - Create and optimize schemas, tables, distribution keys, sort keys, and workload management in Redshift - Implement Redshift Spectrum to query data in S3 using external tables - Implement and maintain Apache Iceberg tables on AWS for schema evolution and ACID operations - Build Iceberg-based ingestion and transformation pipelines using Glue, EMR, or Spark - Develop and optimize Athena queries for operational and analytical reporting - Integrate Athena with S3/Iceberg for low-cost, serverless analytics - Manage Glue Data Catalog integrations and table schema management - Design secure, cost-efficient S3 data lake structures (bronze/silver/gold zones) - Implement data lifecycle policies, versioning, and partitioning strategies - Ensure data governance, metadata quality, and security (IAM, Lake Formation) - Understand telecom-specific datasets such as CDR, xDR, subscriber data, and network KPIs - Build models and pipelines to support network analytics, customer 360, churn prediction, and fraud detection - Tune Spark/Glue jobs for performance and cost - Monitor Redshift/Athena/S3 efficiency and implement best practices - Perform data quality checks and validation across pipelines - Use Git, CodePipeline, Terraform/CloudFormation for infrastructure and deployments (preferred) - Automate pipeline deployment and monitoring (preferred)