Job Description
Our client is seeking an experienced AWS Data Engineer with 4+ years of hands-on Data Engineering experience. The ideal candidate should have strong expertise in Python, PySpark, SQL, and AWS services, with experience building scalable data pipelines and cloud-based data solutions.
nThis role focuses on data ingestion, transformation, storage, and analytics, with an emphasis on data quality, reliability, performance, and cost optimization.
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nKey Responsibilities
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- Design, develop, and maintain scalable data pipelines using Python and PySpark.n
- Build and optimize ETL/ELT workflows for data ingestion, transformation, and processing.n
- Write and optimize complex SQL queries for data transformation, validation, and analytics.n
- Build and manage cloud-based data solutions using AWS Glue, S3, Redshift, Athena, and Lambda, with SNS, SQS, CloudWatch, EventBridge, and API Gateway supporting integration, automation, and monitoring.n
- Ensure data quality and troubleshoot pipelines to improve reliability, performance, and cost efficiency.n




- Collaborate with cross-functional teams to translate business requirements into scalable data solutions.n

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nRequired Skills
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- 4+ years of hands-on experience in Data Engineering.n
- Solid understanding of data ingestion, transformation, ETL/ELT, data quality, data modeling, and pipeline optimization.n
- Strong hands-on experience with Python and PySpark for data processing.n
- Strong practical knowledge of SQL, including complex queries and query optimization.n
- Strong hands-on experience with AWS Glue, S3, Redshift, Athena, and Lambda, along with working knowledge of SNS, SQS, CloudWatch, EventBridge, and API Gateway.n
- Strong problem-solving, communication, and collaboration skills.n

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nNice-to-Have Skills
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- Experience with Amazon Kinesis or Apache Kafka for streaming data pipelines.n
- Exposure to Amazon MWAA or Apache Airflow for workflow orchestration.n
- Famil

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