Job Description & How to Apply Below
We 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.
Core Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines, data lakes, and data warehouse solutions.
- Build and optimize data ingestion frameworks for batch and real-time processing.
- Develop and deploy containerised applications using Docker, Amazon ECR, and Amazon ECS.
- Design and implement RESTful APIs for system integrations using modern frameworks (e.g., FastAPI).
- Implement Infrastructure as Code (IaC) using Terraform and AWS Cloud Formation.
- Establish and maintain CI/CD pipelines for automated build, test, and deployment workflows.
- Ensure adherence to data security, governance, and compliance standards (e.g., encryption, access control).
- Monitor, troubleshoot, and optimise data workflows for performance and reliability.
Required Skills
- Strong proficiency in Python and SQL for data processing and transformation.
- Hands-on experience with FastAPI for API development.
- Experience with distributed data processing frameworks such as PySpark.
- Solid experience with AWS services, including:
Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena
AWS Lambda, AWS DMS, API Gateway
- Experience with containerization and orchestration (Docker, ECS).
- Strong understanding of cloud-native architecture and best practices.
- Excellent problem-solving, communication, and collaboration skills.
Nice to have
- Exposure to Generative AI and Agentic AI concepts.
- Hands-on experience with LLM frameworks such as Lang Chain, Llama Index, or CrewAI.
- Experience working with LLMs and NLP models (e.g., GPT, BERT, LLaMA, Mistral, Gemini).
- Familiarity with LLM-as-a-Service platforms such as AWS Bedrock or Hugging Face.
- Basic understanding of ML model deployment and lifecycle management.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
- 3–5 years of hands-on experience in Data Engineering and AWS cloud environments.
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