Aws Data Engineer

Industry & Sector: IT Services — Cloud & Data Engineering practice focused on building enterprise-scale analytics platforms, cloud data lakes, and production-grade ETL/ELT solutions on AWS for large commercial and enterprise clients.
Location: India (On-site). Role: AWS Data Engineer — hands-on contributor building scalable, secure data pipelines and analytics back-ends.

AWS Data Engineer

• Design, build and operate scalable ETL/ELT data pipelines on AWS (S3, Glue, Redshift, Athena) to ingest, transform and serve structured and semi-structured data.
• Develop and optimise PySpark / Python jobs for performance, cost-efficiency and data quality; troubleshoot failures and implement retry/backfill strategies.
• Implement orchestration and workflow automation using Airflow or AWS Step Functions; manage scheduling, dependencies and SLA monitoring.
• Define data models, partitioning strategies and storage formats (Parquet/ORC), and manage metadata with Glue Data Catalog or Lake Formation.
• Instrument pipelines with monitoring, alerting and observability (CloudWatch, logging, metrics) and own incident resolution and root-cause analysis.
• Apply Infrastructure-as-Code (Terraform/CloudFormation) and CI/CD practices to deploy pipelines securely (IAM, VPC, encryption) and reproducibly.

Must-Have

• 3+ years of hands-on experience as a Data/ETL Engineer on AWS with core services: S3, Glue, Redshift, Athena, Lambda.
• Strong Python and PySpark skills plus advanced SQL for complex transformations and performance tuning.
• Proven experience designing ETL/ELT patterns, data modeling, partitioning and ensuring data quality and lineage.
• Familiarity with orchestration tools (Airflow or Step Functions), version control (Git) and production monitoring (CloudWatch).

Preferred

• Experience with Terraform or CloudFormation, containerization (Docker) and CI/CD pipelines for data workloads.
• Exposure to streaming technologies (Kinesis, Kafka), data cataloging (Glue Data Catalog, Lake Formation) and Redshift performance tuning.

• On-site, hands-on engineering role with high ownership and visible impact on enterprise analytics initiatives.
• Support for professional development and AWS certifications; learning-focused environment with mentoring.
• Collaborative, fast-paced team that values engineering excellence, automation and measurable outcomes.

Why apply: This role is ideal for mid-level data engineers who want deep technical ownership of cloud-native data platforms on AWS, work on end-to-end pipeline delivery, and accelerate their cloud engineering career in an on-site India setting.