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.
Iris Software - Lead Data Engineer - AWS Platform • Delhi, IN
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