

Job Overview:
We are seeking an experienced AWS Data Engineer to join our growing data team. The ideal candidate will have hands-on experience with AWS Glue, Redshift, PySpark, and other AWS services to build robust, scalable data pipelines. This role is perfect for someone passionate about data engineering, automation, and cloud-native development.
Key Responsibilities:
- Design, build, and maintain scalable and efficient ETL pipelines using AWS Glue, PySpark, and related tools.
- Integrate data from diverse sources and ensure its quality, consistency, and reliability.
- Work with large datasets in structured and semi-structured formats across cloud-based data lakes and warehouses.
- Optimize and maintain data infrastructure, including Amazon Redshift, for high performance.
- Collaborate with data analysts, data scientists, and product teams to understand data requirements and deliver solutions.
- Automate data validation, transformation, and loading processes to support real-time and batch data processing.
- Monitor and troubleshoot data pipeline issues and ensure smooth operations in production environments.
Required Skills:
- 5 to 7 years of hands-on experience in data engineering roles.
- Strong proficiency in Python and PySpark for data transformation and scripting.
- Deep understanding and practical experience with AWS Glue, AWS Redshift, S3, and other AWS data services.
- Solid understanding of SQL and database optimization techniques.
- Experience working with large-scale data pipelines and high-volume data environments.
- Good knowledge of data modeling, warehousing, and performance tuning.
Preferred/Good to Have:
- Experience with workflow orchestration tools like Airflow or Step Functions.
- Familiarity with CI/CD for data pipelines.
- Knowledge of data governance and security best practices on AWS.

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