AWS Data Engineer

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