AWS Data Engineer

• Design and develop scalable data pipelines and data engineering solutions on AWS.
• Build and maintain robust ETL/ELT pipelines using Python and AWS services.
• Develop data ingestion, transformation, validation, and processing frameworks.
• Design and implement enterprise AWS Data Lake and Data Warehouse architectures.
• Work with AWS services such as S3, Glue, Lambda, EMR, Redshift, Athena, Kinesis, Step Functions, and ECS/EKS.
• Develop complex data processing solutions using Python, PySpark, and SQL.
• Design data models for structured, semi-structured, and unstructured datasets.
• Optimize data pipelines for performance, scalability, reliability, and cost.
• Implement data quality, governance, lineage, validation, and monitoring processes.
• Develop orchestration workflows using Apache Airflow / AWS MWAA.
• Build CI/CD pipelines for data engineering applications and infrastructure.
• Collaborate with architects, data scientists, ML engineers, application developers, and business stakeholders., * Build data pipelines supporting Machine Learning and Generative AI use cases.
• Prepare, clean, transform, and engineer datasets for AI/ML models.
• Work with LLMs, embeddings, vector databases, and RAG pipelines.
• Experience with Amazon Bedrock and foundation models is highly desirable.
• Develop AI-enabled data processing and automation solutions.
• Integrate pre-trained AI/ML models through APIs.
• Support model training, evaluation, deployment, and monitoring workflows.
• Use Python, Pandas, NumPy, Scikit-learn, and other AI/ML libraries as needed.
• Work with AI coding assistants such as GitHub Copilot, Amazon Q Developer, or Cursor.

• 14+ years of overall IT experience with strong Data Engineering background.
• Extensive hands-on experience with AWS Data Engineering.
• Strong Python programming skills.
• Advanced SQL skills including complex queries, CTEs, window functions, and optimization.
• Strong experience with AWS S3, Glue, Redshift, Lambda, EMR, and Athena.
• Strong experience designing ETL/ELT pipelines.
• Hands-on experience with PySpark / Apache Spark.
• Experience with data lake and data warehouse architecture.
• Experience with Airflow / AWS MWAA.
• Strong understanding of data modeling and database concepts.
• Experience with Git and CI/CD.
• Strong understanding of cloud security, IAM, encryption, and AWS best practices.

AI/GenAI Skills - Preferred

• Generative AI / LLM experience.
• Amazon Bedrock.
• RAG architecture.
• Prompt Engineering.
• Embeddings and Vector Databases.
• LangChain / LlamaIndex.
• OpenAI / Azure OpenAI / Anthropic.
• Pinecone / OpenSearch / Elasticsearch / FAISS.
• Machine Learning and model integration.
• AI/ML API integration.