Description

Responsibilities include:

Design, develop, and maintain scalable ETL/data pipelines using AWS services such as Lambda, Glue, S3, and Redshift.

Build and optimize data transformation processes using Python and PySpark to support analytics and reporting needs.

Develop serverless solutions and real-time data processing workflows leveraging AWS Lambda, Kafka, and Kinesis.

Monitor, troubleshoot, and enhance data pipelines to ensure high performance, reliability, and data accuracy.

Collaborate with cross-functional teams to gather requirements and deliver efficient cloud-based data solutions.

Implement data engineering best practices, including automation, CI/CD, data security, and performance optimization.

Required Qualifications:

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

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

Strong hands-on expertise with AWS services including S3, Lambda, Glue, Redshift, Athena, DynamoDB, and CloudWatch, along with Python and PySpark.

Experience building and optimizing large-scale ETL pipelines, real-time data processing solutions, and data warehousing environments

Key Skills

Education

Any Graduate