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Lambda.\n\n- Build, enhance, and support scalable data ingestion, transformation, and processing pipelines using Python.\n\n- Develop and optimize enterprise data warehouse solutions using Amazon Redshift, including query optimization and performance tuning.\n\n- Design, implement, and manage Amazon DynamoDB data models to ensure high performance, scalability, and reliability.\n\n- Ensure data quality, integrity, security, and consistency across data pipelines and storage solutions.\n\n- Collaborate with application developers, cloud engineers, data architects, and business stakeholders to deliver robust data solutions.\n\n- Support production deployments, monitor pipeline performance, troubleshoot issues, and provide timely resolutions.\n\n- Implement AWS best practices for architecture, security, monitoring, scalability, and cost optimization.\n\n- Optimize ETL/ELT processes to improve performance and maintainability.\n\n- Participate in code reviews, documentation, and continuous improvement initiatives.\n\nRequired Skills :\n\n- Strong hands-on experience with AWS Lambda.\n\n- Proficiency in Python for data engineering and automation.\n\n- Experience with Amazon Redshift for data warehousing, query optimization, and performance tuning.\n\n- Strong knowledge of Amazon DynamoDB and NoSQL database design.\n\n- Experience in building scalable ETL/ELT pipelines.\n\n- Good understanding of AWS services, cloud-native architecture, and serverless computing.\n\n- Knowledge of data quality, governance, and security best practices.\n\n- Strong problem-solving, analytical, and communication skills.\n\nPreferred Skills :\n\n- Experience with additional AWS services such as Amazon S3, AWS Glue, Step Functions, IAM, CloudWatch, SNS/SQS, or EventBridge.\n\n- Familiarity with CI/CD pipelines and infrastructure-as-code tools such as Terraform or CloudFormation.\n\n- Experience working in Agile/Scrum environments.\nSkills\nAWS, Data Engineering, DynamoDB, ETL, NoSQL, AWS Glue, 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