Key Responsibilities:

- Design, develop, and maintain scalable data pipelines on AWS.

- Build and optimize ETL/ELT workflows using AWS Glue and Redshift.

- Develop data processing solutions using SQL, Python, and PySpark.

- Design and maintain data lake and data warehouse architectures.

- Implement data modeling, partitioning strategies, and performance optimization techniques.

- Develop and manage workflow orchestration using AWS Step Functions, Apache Airflow, or Glue Workflows.

- Ensure data quality, security, governance, and operational excellence.

- Collaborate with cross-functional teams to deliver reliable and scalable data solutions.

Required Skills:

- 8-10 years of experience in Data Engineering.

- Strong hands-on experience with AWS Data Services, including :

1. Amazon S3

2. AWS Glue

3. Amazon Redshift

- Proficiency in SQL, Python, and PySpark.

- Strong understanding of ETL/ELT, data modeling, and data partitioning.

- Experience with workflow orchestration tools such as AWS Step Functions, Apache Airflow, or Glue Workflows.

- Strong knowledge of Data Lake and Data Warehouse architectures.

- Experience in performance tuning and optimization of large-scale data pipelines.

- Excellent analytical, troubleshooting, and communication skills.