AWS Data Engineer

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Mindfire Solutions

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Job Description

About the Job

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 CloudFormation.

- 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 LangChain, LlamaIndex, 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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