What are the responsibilities and job description for the AWS Data Engineer position at Lares IT Solutions Inc?
Key Responsibilities
- Design, build, and maintain scalable data pipelines on AWS using services such as S3, Glue, Lambda, Step Functions
- Develop and optimize ETL/ELT workflows, ensuring high data quality, consistency, and availability across analytical and operational systems.
- Work extensively with AWS ECS/ECR to containerize, orchestrate, and deploy data processing and ML/GenAI workloads.
- Build secure and scalable APIs using API Gateway, Lambda, and container-based services for consumption by internal and external applications.
- Architect and manage NoSQL workloads on DynamoDB, including partitions, performance tuning, and data modeling.
- Implement secure authentication and authorization mechanisms using AWS Cognito.
- Configure and manage Route 53, DNS routing, SSL certificates, and integrations for multi-service distributed systems.
- Utilize CloudWatch Logs and Metrics for monitoring, troubleshooting, performance tuning, and building automated alerts.
- Implement AWS Secrets Manager for secure credential and key management across the data ecosystem.
- Design, manage, and optimize AWS OpenSearch
clusters for indexing and querying structured and unstructured data. - Deploy and manage static websites using S3, CloudFront, Route 53, and related AWS services.
- Integrate Generative AI capabilities into data workflows using Amazon Bedrock, AgentCore, custom LLMs, and embeddings.
- Build RAG (Retrieval-Augmented Generation) pipelines leveraging vector databases (e.g., OpenSearch vector engine) to enhance AI-driven search and automation.
- Develop data-driven solutions powering chatbots, summarization tools, document processing pipelines, and enterprise AI applications.
- Collaborate closely with data scientists, ML engineers, and application developers to bring AI/ML and GenAI features into production.
- Ensure compliance with data governance, security, CI/CD, and MLOps best practices across all environments.
Required Skills & Experience
- 7–12 years of total IT experience, including strong hands-on experience as an AWS Data Engineer.
- Expert-level proficiency with AWS services:
- ECS, ECR
- S3, Glue, Lambda, Step Functions
- API Gateway, CloudFront
- DynamoDB, Cognito
- Route 53, CloudWatch
- Secrets Manager
- AWS OpenSearch (including vector search capabilities)
- Solid experience designing and operating containerized workloads using Docker ECS/Fargate.
- Strong Python skills for building data pipelines, automation, and backend integrations.
- Experience integrating and implementing Generative AI solutions using:
- Amazon Bedrock, Bedrock AgentCore
- LLM orchestration tools (LangChain, LlamaIndex)
- RAG architectures
- Embeddings and vector search
- Deep understanding of data modeling, distributed systems, partitioning, performance optimization, and streaming/batch data processing.
- Prior experience deploying and managing static web applications on AWS (S3 CloudFront Route 53).
- Familiarity with CI/CD pipelines using CodePipeline, GitHub Actions, or Jenkins.
- Strong understanding of IAM policies, roles, cross-account access, and cloud security best practices.
- Excellent communication and cross-team collaboration skills.
Preferred Qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field (Required)
- Master’s degree in Data Engineering, Cloud Computing, or AI (Preferred)
- AWS Certifications such as:
- AWS Certified Data Engineer – Associate
- AWS Certified Solutions Architect
- AWS Machine Learning Specialty
- Experience with AI/ML model deployment using SageMaker or Bedrock Agents