Description

Build and maintain event-driven data pipelines and streaming solutions on AWS.

This role is on-site.

Responsibilities

• Build and maintain event-driven data pipelines using AWS Kinesis, MSK/Kafka, Lambda, Step Functions, SQS/SNS, and Glue/EMR.
• Develop 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/or Redshift.
• Implement and optimize Spark-based data transformations, partitioning strategies, and data processing frameworks.
• Create reusable frameworks, internal libraries, and CI/CD pipelines for automated deployments.

Required Skills

• 10+ years of overall experience, with 7+ years 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.
• Experience with CI/CD, Git, and Infrastructure-as-Code (Terraform or CloudFormation).
• Strong understanding of distributed systems, Spark performance tuning, data modeling, and cloud cost optimization.
• Knowledge of data security, encryption, networking, and compliance best practices in cloud environments.

Preferred Skills

• Experience working on Java is an advantage.

Key Skills

Education

Bachelor's degree