Description

Responsibilities include:

Design, develop, and maintain scalable ETL pipelines to support enterprise data integration and analytics.

Build and optimize cloud-native data solutions using AWS services such as S3, Lambda, Glue, and Redshift.

Develop data transformation and processing frameworks using Python, PySpark, and SQL.

Implement real-time and batch data ingestion workflows leveraging Kafka/Kinesis and AWS technologies.

Monitor, troubleshoot, and enhance data pipeline performance, reliability, and scalability.

Collaborate with cross-functional teams to deliver secure, high-quality data solutions that meet business requirements.

Required Qualifications:

Bachelor’s degree in computer science, Information Technology, Engineering, or a related field.

10+ years of experience in data engineering, ETL development, and cloud-based data platforms.

Strong hands-on expertise with Python, PySpark, SQL, AWS Lambda, S3, Glue, Redshift, and DynamoDB.

Experience building and optimizing large-scale data pipelines and real-time data processing solutions using Kafka/Kinesis in AWS environments

Key Skills

Education

Any Graduate