AWS Data Engineer

About the Job\nWe are seeking a skilled Data Engineer to architect, build, and optimise scalable data platforms on cloud infrastructure. The role involves close collaboration with cross-functional teams to deliver robust, secure, and high-performance data solutions that support analytics and business operations.\n\nCore Responsibilities\n- Design, develop, and maintain scalable ETL/ELT pipelines, data lakes, and data warehouse solutions.\n- Build and optimize data ingestion frameworks for batch and real-time processing.\n- Develop and deploy containerised applications using Docker, Amazon ECR, and Amazon ECS.\n- Design and implement RESTful APIs for system integrations using modern frameworks (e.g., FastAPI).\n- Implement Infrastructure as Code (IaC) using Terraform and AWS CloudFormation.\n- Establish and maintain CI/CD pipelines for automated build, test, and deployment workflows.\n- Ensure adherence to data security, governance, and compliance standards (e.g., encryption, access control).\n- Monitor, troubleshoot, and optimise data workflows for performance and reliability.\n\nRequired Skills\n- Strong proficiency in Python and SQL for data processing and transformation.\n- Hands-on experience with FastAPI for API development.\n- Experience with distributed data processing frameworks such as PySpark.\n- Solid experience with AWS services, including:\nAmazon S3, AWS Glue, Amazon Redshift, Amazon Athena\nAWS Lambda, AWS DMS, API Gateway\n- Experience with containerization and orchestration (Docker, ECS).\n- Strong understanding of cloud-native architecture and best practices.\n- Excellent problem-solving, communication, and collaboration skills.\n\nNice to have\n- Exposure to Generative AI and Agentic AI concepts.\n- Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or CrewAI.\n- Experience working with LLMs and NLP models (e.g., GPT, BERT, LLaMA, Mistral, Gemini).\n- Familiarity with LLM-as-a-Service platforms such as AWS Bedrock or Hugging Face.\n- Basic understanding of ML model deployment and lifecycle management.\n\nQualifications\n- Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.\n- 3–5 years of hands-on experience in Data Engineering and AWS cloud environments.