Description

You will architect and maintain event-driven data pipelines and data lake structures using AWS services.

This role is on-site.

Responsibilities

• Architect, build, and maintain event-driven data pipelines using AWS Kinesis, MSK/Kafka, Lambda, Step Functions, SQS/SNS, and Glue/EMR.
• Develop and optimize ETL/ELT workflows using Python and PySpark, ensuring performance, scalability, and cost efficiency.
• Design and manage data lake and warehouse structures using S3, Glue Catalog, Athena, and Redshift.
• Implement streaming solutions with checkpointing, stateful transformations, idempotency, and schema evolution.
• Orchestrate workflows using Airflow or Step Functions and write SQL for analytical workloads.

Required Skills

• 15+ years of professional experience in Data Engineering.
• Strong expertise in Python and PySpark for large-scale data processing.
• Advanced hands-on experience with AWS services: S3, Glue, EMR, Lambda, Step Functions, Kinesis/MSK, DynamoDB, Athena, Redshift.
• Deep experience building event-driven and streaming data pipelines.
• Strong SQL experience for analytical and ETL workloads.
• Hands-on experience with workflow orchestration tools such as Airflow or Step Functions.

Key Skills

Education

Any Graduate