Key Responsibilities :

- Design, develop, and maintain serverless data solutions using AWS Lambda.

- Build, enhance, and support scalable data ingestion, transformation, and processing pipelines using Python.

- Develop and optimize enterprise data warehouse solutions using Amazon Redshift, including query optimization and performance tuning.

- Design, implement, and manage Amazon DynamoDB data models to ensure high performance, scalability, and reliability.

- Ensure data quality, integrity, security, and consistency across data pipelines and storage solutions.

- Collaborate with application developers, cloud engineers, data architects, and business stakeholders to deliver robust data solutions.

- Support production deployments, monitor pipeline performance, troubleshoot issues, and provide timely resolutions.

- Implement AWS best practices for architecture, security, monitoring, scalability, and cost optimization.

- Optimize ETL/ELT processes to improve performance and maintainability.

- Participate in code reviews, documentation, and continuous improvement initiatives.

Required Skills :

- Strong hands-on experience with AWS Lambda.

- Proficiency in Python for data engineering and automation.

- Experience with Amazon Redshift for data warehousing, query optimization, and performance tuning.

- Strong knowledge of Amazon DynamoDB and NoSQL database design.

- Experience in building scalable ETL/ELT pipelines.

- Good understanding of AWS services, cloud-native architecture, and serverless computing.

- Knowledge of data quality, governance, and security best practices.

- Strong problem-solving, analytical, and communication skills.

Preferred Skills :

- Experience with additional AWS services such as Amazon S3, AWS Glue, Step Functions, IAM, CloudWatch, SNS/SQS, or EventBridge.

- Familiarity with CI/CD pipelines and infrastructure-as-code tools such as Terraform or CloudFormation.

- Experience working in Agile/Scrum environments.